gp3_CESM_10year_equinox_pr_change_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-05-10T23:06:57.242Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import xarray\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mp\n",
    "import datetime\n",
    "import matplotlib.colors\n",
    "import scipy.stats\n",
    "import itertools\n",
    "import string\n",
    "import matplotlib.patches\n",
    "\n",
    "import glob\n",
    "import pandas\n",
    "\n",
    "import mpl_toolkits\n",
    "import cartopy\n",
    "\n",
    "DEGREE_SIGN = u'\\u00B0'\n",
    "mp.rcParams.update({'mathtext.default': 'regular'})\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import clm400 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-05-10T23:06:58.141Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.10years.cam0400.clm0400/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-05-10T23:06:59.141Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_cam_mfds = xarray.open_mfdataset(data_dir + 'atm/hist/*.nc')#, decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:25:15.738441Z",
     "start_time": "2018-04-27T04:25:15.735393Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_400 = cont_cam_mfds['PRECC']\n",
    "PRECL_400 = cont_cam_mfds['PRECL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:25:15.829420Z",
     "start_time": "2018-04-27T04:25:15.740435Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_400 = PRECC_400+PRECL_400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:26:35.104977Z",
     "start_time": "2018-04-27T04:26:35.098907Z"
    }
   },
   "outputs": [],
   "source": [
    "time_400 = cont_cam_mfds['time'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import clm1000 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:22:28.176776Z",
     "start_time": "2018-04-27T04:22:28.170662Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = '/beegfs/DATA/pritchard/blangenb/CESM_ARCHIVE/B_1850_CAM5.10years.cam0400.clm1000/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:24:12.160578Z",
     "start_time": "2018-04-27T04:23:58.014677Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/xarray/coding/times.py:111: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy netcdftime.datetime objects instead, reason: dates out of range\n",
      "  result = decode_cf_datetime(example_value, units, calendar)\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy netcdftime.datetime objects instead, reason: dates out of range\n",
      "  return self.func(self.array[key])\n"
     ]
    }
   ],
   "source": [
    "phys_cam_mfds = xarray.open_mfdataset(data_dir + 'atm/hist/*.nc')#, decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:35:27.292484Z",
     "start_time": "2018-04-27T04:35:27.284648Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECC_1000 = phys_cam_mfds['PRECC']\n",
    "PRECL_1000 = phys_cam_mfds['PRECL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:35:28.513686Z",
     "start_time": "2018-04-27T04:35:28.507036Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_1000 = PRECC_1000+PRECL_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:35:30.337749Z",
     "start_time": "2018-04-27T04:35:30.331879Z"
    }
   },
   "outputs": [],
   "source": [
    "time_1000 = phys_cam_mfds['time'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:35:34.549890Z",
     "start_time": "2018-04-27T04:35:34.537999Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_1000_sa = PRECT_1000.sel(lat=slice(-30.,30.),lon=slice(270.,330.))\n",
    "PRECT_400_sa = PRECT_400.sel(lat=slice(-30.,30.),lon=slice(270.,330.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:59:06.782739Z",
     "start_time": "2018-04-27T04:59:06.757001Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_400_sa_tm = PRECT_400_sa.mean(dim='time')\n",
    "PRECT_1000_sa_tm = PRECT_1000_sa.mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:59:43.044724Z",
     "start_time": "2018-04-27T04:59:08.517859Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECT_sa_tm_diffs = PRECT_1000_sa_tm.values - PRECT_400_sa_tm.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T04:59:43.231140Z",
     "start_time": "2018-04-27T04:59:43.049261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3651, 32, 25)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.contourf(PRECT_sa_tm_diffs*84600.*1000.)\n",
    "mp.colorbar()\n",
    "PRECT_400_sa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T05:23:52.357701Z",
     "start_time": "2018-04-27T05:23:51.978521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,1.02,'10-year mean $\\\\Delta$ precip.')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "map_crs = cartopy.crs.PlateCarree()\n",
    "data_crs = cartopy.crs.PlateCarree()\n",
    "\n",
    "fig = mp.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111, projection=map_crs)\n",
    "ax.coastlines()\n",
    "\n",
    "ax.set_extent([270,330,-27,20])\n",
    "\n",
    "levels = numpy.arange(-2.5,2.6,0.25)\n",
    "contour_map = ax.contourf(PRECT_400_sa['lon'].values, \\\n",
    "                          PRECT_400_sa['lat'].values, \\\n",
    "                          PRECT_sa_tm_diffs*86400*1000, \\\n",
    "                          cmap='RdBu', \\\n",
    "                          levels=levels, \\\n",
    "                          transform=data_crs, \\\n",
    "                          extend='both')\n",
    "\n",
    "cbar = mp.colorbar(contour_map)\n",
    "cbar.set_ticks(levels[2::4])\n",
    "cbar.ax.tick_params(labelsize=fontsize)\n",
    "\n",
    "ax.text(s='10-year mean $\\Delta$ precip.', \\\n",
    "        x=0, y=1.02, \\\n",
    "        ha='left', va='bottom', \\\n",
    "        transform=ax.transAxes, \\\n",
    "        fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate precipitable water and save"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:22:11.678235Z",
     "start_time": "2018-04-16T21:22:11.597602Z"
    }
   },
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/400/ens_mean_wrfout_files'\n",
    "file_list = [root_dir+'/wrfout_d01_1979-01-'+'{:02d}'.format(day)+'_00:00:00_ens_mean.nc' for day in range(1,12)]\n",
    "dataset_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:26:31.853045Z",
     "start_time": "2018-04-16T21:22:38.818879Z"
    }
   },
   "source": [
    "cont_prw = wrf.getvar(dataset_list, 'pw', timeidx=wrf.ALL_TIMES)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:26:31.914028Z",
     "start_time": "2018-04-16T21:26:31.855423Z"
    }
   },
   "source": [
    "root_dir = '/beegfs/DATA/pritchard/blangenb/WRF_ARCHIVE/CHEYENNE/FMAASO/1000/ens_mean_wrfout_files'\n",
    "file_list = [root_dir+'/wrfout_d01_1979-01-'+'{:02d}'.format(day)+'_00:00:00_ens_mean.nc' for day in range(1,12)]\n",
    "dataset_list = [netCDF4.Dataset(file) for file in file_list]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:30:38.874748Z",
     "start_time": "2018-04-16T21:26:31.915665Z"
    }
   },
   "source": [
    "phys_prw = wrf.getvar(dataset_list, 'pw', timeidx=wrf.ALL_TIMES)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:30:44.828906Z",
     "start_time": "2018-04-16T21:30:44.746250Z"
    }
   },
   "source": [
    "numpy.save('DATA_prw_400.npy', cont_prw)\n",
    "numpy.save('DATA_prw_1000.npy', phys_prw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:30:47.830093Z",
     "start_time": "2018-04-16T21:30:47.736552Z"
    }
   },
   "outputs": [],
   "source": [
    "cont_prw = numpy.load('DATA_prw_400.npy')\n",
    "phys_prw = numpy.load('DATA_prw_1000.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get time stamps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:46:33.651543Z",
     "start_time": "2018-04-16T21:46:33.644286Z"
    }
   },
   "outputs": [],
   "source": [
    "time_datetimes = pandas.to_datetime([i.decode('utf-8') for i in time], format = '%Y-%m-%d_%H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:46:34.206567Z",
     "start_time": "2018-04-16T21:46:34.176109Z"
    }
   },
   "outputs": [],
   "source": [
    "skip_time = 24\n",
    "n_intervals = 8\n",
    "hour_strings = ['00','03','06','09','12','15','18','21']\n",
    "\n",
    "times_00 = [t.hour in [23,0,1] for t in time_datetimes[skip_time:]]\n",
    "times_03 = [t.hour in [2,3,4] for t in time_datetimes[skip_time:]]\n",
    "times_06 = [t.hour in [5,6,7] for t in time_datetimes[skip_time:]]\n",
    "times_09 = [t.hour in [8,9,10] for t in time_datetimes[skip_time:]]\n",
    "times_12 = [t.hour in [11,12,13] for t in time_datetimes[skip_time:]]\n",
    "times_15 = [t.hour in [14,15,16] for t in time_datetimes[skip_time:]]\n",
    "times_18 = [t.hour in [17,18,19] for t in time_datetimes[skip_time:]]\n",
    "times_21 = [t.hour in [20,21,22] for t in time_datetimes[skip_time:]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lat indices subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:46:35.618045Z",
     "start_time": "2018-04-16T21:46:35.612489Z"
    }
   },
   "outputs": [],
   "source": [
    "lat_hi = 10.\n",
    "lat_lo = -10.\n",
    "# subset between lat_hi and lat_lo\n",
    "lat_indices = (xlat<=lat_hi)&(xlat>=lat_lo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # calculate prw diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:58:29.976101Z",
     "start_time": "2018-04-16T21:58:29.784038Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_vint_diffs = phys_prw_array - cont_prw_array\n",
    "#qvapor_vint_diffs_landmask = numpy.copy(qvapor_vint_diffs)\n",
    "#qvapor_vint_diffs_landmask[:,landmask==0] = numpy.nan\n",
    "\n",
    "qvapor_vint_diffs_time_mean = numpy.mean(phys_prw[skip_time:,:,:] - cont_prw[skip_time:,:,:], axis=0)\n",
    "#qvapor_vint_diffs_time_mean_landmask = numpy.copy(qvapor_vint_diffs_time_mean)\n",
    "#qvapor_vint_diffs_time_mean_landmask[landmask==0] = numpy.nan\n",
    "\n",
    "cont_prw_time_mean = numpy.mean(cont_prw[skip_time:,:,:], axis=0)\n",
    "#cont_prw_time_mean_landmask = numpy.copy(cont_prw_time_mean)\n",
    "#cont_prw_time_mean_landmask[landmask==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:58:31.038465Z",
     "start_time": "2018-04-16T21:58:30.973523Z"
    }
   },
   "outputs": [],
   "source": [
    "qvapor_diffs_vint_alltime = phys_prw-cont_prw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# apply landmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:11:59.909359Z",
     "start_time": "2018-04-18T18:11:51.233065Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_400[:,:,landmask==0] = numpy.nan\n",
    "wa_1000[:,:,landmask==0] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:12:02.273651Z",
     "start_time": "2018-04-18T18:12:01.309163Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_400_lat_subset = wa_400[:,:,lat_indices[:,0],:]\n",
    "wa_1000_lat_subset = wa_1000[:,:,lat_indices[:,0],:]\n",
    "wa_diffs_lat_subset = wa_1000_lat_subset-wa_400_lat_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:12:15.323077Z",
     "start_time": "2018-04-18T18:12:12.738803Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: Mean of empty slice\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"\n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: Mean of empty slice\n",
      "  \n",
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: Mean of empty slice\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "wa_diffs_time_mean = numpy.nanmean(wa_diffs_lat_subset[skip_time:,:,:], axis=0)\n",
    "wa_diffs_time_lat_mean = numpy.nanmean(wa_diffs_time_mean, axis=1)\n",
    "wa_diffs_lat_mean = numpy.nanmean(wa_diffs_lat_subset, axis=2)\n",
    "\n",
    "wa_400_time_mean = numpy.nanmean(wa_400_lat_subset[skip_time:,:,:], axis=0)\n",
    "wa_400_time_lat_mean = numpy.nanmean(wa_400_time_mean, axis=1)\n",
    "wa_400_lat_mean = numpy.nanmean(wa_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:30:08.686411Z",
     "start_time": "2018-04-18T17:30:07.855462Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_lat_subset = ptot_400.values[:,:,lat_indices[:,0],:]\n",
    "ptot_1000_lat_subset = ptot_1000.values[:,:,lat_indices[:,0],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:35:07.879754Z",
     "start_time": "2018-04-18T17:35:07.025659Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_time_mean = numpy.mean(ptot_400_lat_subset[skip_time:,:,:], axis=0)\n",
    "ptot_400_time_lat_mean = numpy.mean(ptot_400_time_mean, axis=1)\n",
    "ptot_400_lat_mean = numpy.mean(ptot_400_lat_subset, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:35:11.316025Z",
     "start_time": "2018-04-18T17:35:11.312318Z"
    }
   },
   "outputs": [],
   "source": [
    "xx,zz=numpy.meshgrid(xlong[0,:],ptot_400_time_lat_mean[:,:].mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:45:56.761467Z",
     "start_time": "2018-04-18T18:45:56.755051Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "270.2386779785156"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlong.min()+360+65"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:47:31.720120Z",
     "start_time": "2018-04-18T18:47:30.617315Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x180 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 12\n",
    "\n",
    "fig = mp.figure(figsize=(4,2.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "levels = numpy.arange(-1.25,1.26,0.25)\n",
    "\n",
    "contour_plot = ax.contourf(xx+360., ptot_400_time_lat_mean/100., wa_diffs_time_lat_mean*1000., \\\n",
    "                           levels=levels, \\\n",
    "                           cmap='RdBu', \\\n",
    "                           extend='both')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylim(1000,100)\n",
    "#ax.set_xlim(xlong.min()+65.+360.,xlong.max()-2+360.)\n",
    "ax.set_xlim(274.5,325.)\n",
    "#ax.set_xlim(280,320)\n",
    "\n",
    "ax.text(s='Mean $\\Delta$ vertical velocity',\\\n",
    "       x=0, y=1.02, \\\n",
    "       ha='left', va='bottom', \\\n",
    "       fontsize=fontsize, \\\n",
    "       transform=ax.transAxes)\n",
    "\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.axhline(y=860.,lw=1,ls='--',c='0.1')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax_pos = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_pos.x1+0.05, ax_pos.y0, 0.03, ax_pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks(levels[1::2])\n",
    "cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('mean_vertical_velocity_change.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pull out w for p at 700hPa and below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:52:27.139320Z",
     "start_time": "2018-04-18T17:52:25.057931Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_alltime = wa_1000-wa_400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:54:01.662783Z",
     "start_time": "2018-04-18T17:54:01.657550Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241, 29, 179, 289)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptot_400.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:56:18.327341Z",
     "start_time": "2018-04-18T17:56:16.588718Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_surf_to_700 = numpy.copy(wa_diffs_alltime)\n",
    "wa_diffs_surf_to_700[ptot_400<70000.] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:57:05.863759Z",
     "start_time": "2018-04-18T17:57:04.243858Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_700_to_top = numpy.copy(wa_diffs_alltime)\n",
    "wa_diffs_700_to_top[ptot_400>70000.] = numpy.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:57:10.426283Z",
     "start_time": "2018-04-18T17:57:06.938436Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/blangenb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: Mean of empty slice\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "wa_diffs_surf_to_700_map = numpy.nanmean(wa_diffs_surf_to_700, axis=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T17:57:17.604334Z",
     "start_time": "2018-04-18T17:57:14.410450Z"
    }
   },
   "outputs": [],
   "source": [
    "wa_diffs_700_to_top_map = numpy.nanmean(wa_diffs_700_to_top, axis=(0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot changes in vertical velocity at different levels in atmosphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:44:53.186459Z",
     "start_time": "2018-04-18T18:44:51.277490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'$-20$°'), Text(0,0,'$-10$°'), Text(0,0,'$0$°'), Text(0,0,'$10$°')]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "lon_offset = 25.\n",
    "\n",
    "diff_levels = numpy.arange(-1.25,1.26,0.125)\n",
    "\n",
    "fig = mp.figure(figsize=(5,3))\n",
    "ax = fig.add_subplot(111, projection=cartopy.crs.PlateCarree())\n",
    "ax.set_extent([273,329,-30,15], crs=cartopy.crs.PlateCarree())\n",
    "ax.coastlines(color='0.1')\n",
    "contour_plot = ax.contourf(xlong,xlat,wa_diffs_700_to_top_map*1000., \\\n",
    "                             levels=diff_levels, extend='both', \\\n",
    "                             #vmin=diff_levels.min(),vmax=diff_levels.max(),\\\n",
    "                             cmap='RdBu')\n",
    "\n",
    "ax.text(s=\"$\\Delta$ w 700-200 hPa layer\", x=0, y=1.01,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "#ax.background_patch.set_facecolor('0.5')\n",
    "\n",
    "ax_pos = ax.get_position()\n",
    "cbar = mp.colorbar(contour_plot)\n",
    "cbar.ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar.set_ticks([-1.5,-1,-0.5,0,0.5,1.,1.5])\n",
    "\n",
    "ax.set_yticks([-20,-10,0,10])\n",
    "ax.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n",
    "\n",
    "#fig.savefig('hourly_ptot_below860_change_hour'+ '{:02d}'.format(t)+'.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "#mp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-18T18:45:22.876044Z",
     "start_time": "2018-04-18T18:45:20.565282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'$-20$°'), Text(0,0,'$-10$°'), Text(0,0,'$0$°'), Text(0,0,'$10$°')]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zETkTc0lISQWg8udtPPHQPdy88H6effQhjpv5GxyhodhB3Jz+RMCJbcxWcFt5Mc2lpQCEp6fr8oR5G/HXSjbqCuYHgRm+Ii19aZ52kktPWLy+Jja7KyH4ayF3x3qXz0t3vDEO1ttAAF2j15Dy7Y8+zV9u+wNfrfwQmMXTZ3Rq/s6OZra5NUmjujCfLWPiaWnbR1PLXiK70YdMXv6gK3LvSmN21RVTH6UR24ZPlrNg4b2MOGYsL77zAXv7RvDJ519SXfw9y5Ys5vIrr9IfPDGZaXR0qKRmjyPtyKN4+KH7GTpqDH36hHBERhajjsz0q/+u3PfMJCb8vQGaS0uxumUFcbss5As7GOQFm/VglEnkeQA7TdWMnvpdfY1R/m4mZisCt5u09Mt90c9x9qQnxoGMIwD/0GtI+dJjUxj7j39wycUXU79rJzMX3ECE2kJTUDhvFe/lmeWbOGF0EplT5tC34H1+f/IEIsLDLNsyT2DJ6EqLs1su31BdWS3LPv4P7y95FzUmlS17QthVWkx443Yevus2Rk+aBkBLSwsX3Xg5Y8eNZf7Fv+PEGScBHaxYvZbVK5fT3t5OxfafOPeK6/nny8+w8qMP2dfWxs9bvuP3C+5m5onTDH3b6aRWVpS8rThHfSXLua282ODVIixkQcj1xeWEpxcTYmrDzn2wPTKRYLwnC8V5BaMVLWQToNOFsJvYH0Kx06utpBZ/fX9l33RzeL9ox+ph2lPYHy+OX9rz49eGXkPKANnpiTz98htccOYpPJmcQMeRk1n80RY95HrTsmhuWHAJrauT2FFf1eXrs+wXLHxFbSPc6NrS8prI8sykd3R08Pxzz7Bi+ce43W52VlVx9RWX8d9yN5Xfb6XV7eDM259ldVAYFVu1qMRz43Zz56038eKrr5M2IIp7b7uZFf/9lNT0gQyfMJXrnnyTI4/MAOCUa/5MUbWWb3pH0Xc8cc9tfLbpR+698UrL8ZlhpSO66or17wZrDgihk4iV2GSdsPuOzAWg/pH7DJZ0CPaShE5eHmKGzt9F3VWOnFhWtGHWuf3LIuJ9LN2BlUdOV+2JcHhLacuT6U5e1pME3NXD1mr7rvRjS6+RAHocvYaUxcVf83MxexobeG7J11TXfW7Yxt1Yy+KPtnDtab/lxZsuIn9rJTmDOl/FZUtEWFwyfOWrtYva06PyIr2zyzk7mmnas4c/3ryAnbX1XPWn2wnv148mHMweFs+lwFvFe1m6qYK//PkJw75Lr72c1084lhdffpkLfncFV1/4Wz7852LaotPIL6u3PEeFlQ2UNfanZfB5rHtnEffFJfKXy870mhzy1yVO3VVO2759NKn9aNqzh+bmZlr27kVtaMBd9DPK7n10ROxiX8ooXnj9H2zI/5zmHds4fmw2z0wzWtIhYCBP+UEInSk5xbqgkg36trJ+bX5AdIdk5YlBs3VtpbNb/fb+JpJydjTrKUkFujOJtj/b7Q9J+rLozW8TARL+ZdBjpKwoyhrgj6qq5nu+zwCeBJKAD4GLVVVtVBQlCHgNmAHcq6rqw93p58TxR5M2eiJbizZD2GCv9cWfvMer8XOZfvY8nnnhJXLuv1Vf5xUiLS23i/IT28sXaHde2y658hrS01K5769P0Njeh4pGNzvq97LN7SDDXco5SjlFiele+20qqEKZNpCQ4GAcfUOYMn4skckZbN6p3RhF1U2kujon9worG/js429oKN8CgMMRz+dLXuXGbQVEOEJQgoIo+KmCn3+upLVhN7gb6D8gnscef5KRQzr1X3Hj/W1NCY8suI729nb6OMJoD+pL37AwoiOdRPRR+XHLFlQVQvr0ISd3CmtWrSD8uGtxDOpLUfE7XscjiBnsrWYZ8kPTbtLQX0I2W3jCurYKFLJqv6sJNF+we8vylfLTqj9/U6fawWqsVgFPXe1zMNEvLpW+MQMt13W4m9jzi47m0OGASdlDvr8BJknL+gNvAhcBnwLPAXcB1wEnAj8AlwCLFUV5VlVVv2s9KYpCjNrEeQtvYXVzHEsee8Zrm03LljHlpnl8+8YLLF/9OdNmn95lu74CSqy8KaxuEvON19DYyOZvv+PV558htLmM6MhE0hwwPjaUe/LLeeSeV5h50dk8fUYq90n7ZU6Zw7c3DGR7RRVllVVkJidQ39iEq66YSX2hflAmNSkuKhrdrCraBUBZVZOhGkvk4On0Ca+nMTSSrXXN/PDFRlpb2+gI6ktHUCodTgf1u3cy/7yzefC+e8mecDzBwcEQFs7851ex7vHb2BecwB7XEJAKBOwgmmNia0hpqOf3Z57EyROO5q2vt/FzvZtSNQpqy+nndHqdX7OcEbSrHFdssvFhWGd8GJqJ2FfeEjOsdHMBfyIM/Z3o6sq1Tn6DkiWFrsLczdgfMvblYueLpP31gw7g4KAnLOVJnnZkYj0V+FpV1fcAFEW5H81avg4tNCfYs48CdKseVVNQOO59HQzut4/fzchg4vdT2fre32kOHwiK5nbtbqzl8QcXMfn0P/CH++7jsf4J5E6ccKDHaQuzfie+b9r8HUcNG0pISAjtIYmmCcYQPauds6OZuCE5VBfmMzZvLp/OaGVvZSlXLniYa357Cjua2/jHinyOO+lU1LVvE5lczhUF0Xz28TdUF+bjiIjW80gLRA4cRmS8k6NGJxFc2UDBjy1eRQL2hh3BsNMv4KG/PEx11Y3EJqeSlJRMwcavaAsZQHN4uoGQQTu3G1v7kNzSypgJE9i0o5p7H3qEGWecS8X3X9BatRXnkHiaS0t1D4zm0lLqizulIv12NgWaWOnHvmD22vAVCNSdwgV2hLO/bmK+gly6YwF3NcHmr/Xuqw15XUCuODQ4YFJWVfU2AEVRTpAWZwMbpe/fAvGKojiB5cB84Cdgoaqqlm8liqJcClwqLxMa5Blz53P1H24m/qhjqdgdQb89RQS3N9MYOULf1t1Yy4p3V5A2+iKu/MOt/GXh/3HSiSeYuzEkGBc3rkwIZmlDQK7YYb7BCNMmyjZu2sTokdnG4/K0ffuwNm7/1/Wou8p5szhJlx1OGJ3E9kULWRLSnwEx0Vx06gnsSs3hzHPO4+pHXuLxY53ctTOdJY89aDhWmZwnzRjF7OwkZg2KJuzTlyAWQh47gY6MMWxzOwyadKorlO2zpvD++mKaq8uJaN/NTb/7A9NHZukJk45/epMhAVSLux33oNmcfMHV9O3TB2dYKOvfX8xvcoYzJmcYEV/8QH1xCOHpnZOFoHlluDKTNatZSqdqPudWUHeVE2SaAxAk3lZeTN+RubZJj6w8T3yRtAt7acSOqLqqXGKWv6yyAB5olRpf8IewvaJRww56vosALHCwJvoigW3ii6qqbkVR9gGRHqkir6sGVFV9Dk32QFEUgzU9dcZMcseP5ea7H4QN/4aogTgaymjdW4XbMUC3mAG2fbuZuMyzuGXh/bQ07+U3p83u0mpqj0zkh9o2Hn/0r6hqB/1j4rh0bh5xAwZYToZYhVa3RyayvehHjh09wrBcDrQISc4k/KLFwGJAS9L//MurmfvYs+xZvJjKwg9pHHQcDiA+VfO0cN32FeBdpxC0qity4djN557G5nVlAGxpaqW2VRtnTnQYp9w5m6jT5lHiSAfgqPR4SI8nK85JqiuU6MUL+cuN/+SM6Rl8uvBG7hp9E/fd3Pkg+HnbTuKOvZlbrpxMRFsT6vZNPHDffezdsp3zEgcAnS5yAq7MZC9ilifxrM6TgOx2J/tLg3VIt4BVBJy5D6sHgj+yiD/LexLmB053deaurHy7NAFWHhy+3EYPNczzW6Z1lnNdv/AQfaLHqlkrilIK/FZV1XxFUR4CWlVVXeBZ1xdwA+GqqrbsR9tqTcMer9e+ffv28eizzzNuxm/4ZOUKvvp4CbXbimjul8He0BTDq3eoow+D+Jar8mZz/pSjtXY9r7uyFdGohPHM0tW88sg99EtIp1/iQH5a9iIA0bFxDMwcxMzTTmfytOkkusL0scio2V7E6y8+y8r//pdHrp3PCceOAjqj4uqLy0m543EGXv5Pgw4MWhpSgIgBcTR//Gcu+/ODnHn8eM6ccRxbO46ko08/y3MkKnnPXPcYpR9/A8DukjrDNu98X63/H903mKlZ0WRMH0TitIkETzY+JzfPPYfdJXVkTB+EKzOZiPP/xFvFe7n44tu9+k0fmQXAzScewcd/u5MvPvucBzNTyZ6labeCiMX/ALuLqkmfMUr3d5aJ0Y6QBewsbV9JjLoDc/++JhntfJetthXwN4zb13ai4EFShMOyErldP10RqPl4/G3jQKtZK4rydOyJN1/ua6KvasmN/1FVdbqPNsT81mXAeDMpe+a6ijHOdZWrqnrd/o77YOBgWcqFaLqywFBg6/4QsoD5Ca1ZB8E8ccN8moLCyc06Dy47j9VfbOCWq35PSGutJmd4rOa97n1UpZ3Gg88v4ogJsxgQGcqRO4rpg+YeVdG0jwUPPsaajz5EVUJIP+4UEnNOBlVl0sw5TB08gAjVzVNLVvDY31/isWdf4Pr7n+DM0WkGC+O/6/J54rYbuHT+BVx73hySdxfrpPLl0DwYqoWEn7/kBy9CBi3DXWTyYJSgYGadO5+3n/oL77weT11rHzpCrQn5lvtvIivOyblxu1EnTmPUafMA2PaINn2YOG0izaWllP15Kfm12k9Q29rOO99Xk1PVxIiiatItLFAB18RpvOlx3RubN5dNy5bhbqzFERGtH0P6yCzqlDAWPvYcV54+g+0RIaRJOrJMzLuLqumfFQd0TgB6Z9H1JmOxvb6fZ7xmuQmsA1HM21qRuNV+Zoval1eG1cOgKz93u5B9X8Qs5+W2I1qr/c0yRldh4HZt+Or3EMJqfkuGr7muwwYHi5TfAx5SFGUymp58L/BGTzVudmQXgRo1Le1kDRvB6//+D+fPmk5I227a+sbo+1Vv+5H+AzK48KxTUdv3MXHKNOZOz2FzZSNvvP4G+xKGUx81jhZ3Ox31CbS/+xcqvv+a9ajUnDSbqOPy2FCbhDrmStzfvMO9v7+Afo8+wVljMiEsnNc++A+P3XkLCx/4C2cdP46gkg3Ub9Qs4/ZbnuXca7XkQZEJCTRUVVkem5iMe+aqCcSEjOdf/14PDaHU9vF+RY8bkkPReW20TNByYtQTTY0jnfyyeoqqm7h24fOEffoSIcmZuJIzSUn4hOimVtvzapYb+mdE4cpM5qfkiTzz4npKC4poKN/CDQsuobCyQfd8qS7MJ31kFkXVTWx2hTJq3Di2bf2WyZ52dhdVG/72z4rTCVrv18dDwTw+edJQDueWiVUmdCt5w6u0lYWfuS/4YyGb++tOW3bfDetMGVz9yZxn1695mTlIpLueHIcCNvNbMmznurrjAXawcVBIWVXVGkVRLgAWAXHA22Dw+uo2fCVJMS+L79eHSEcHLf2iaWszbrulLg5H2hlEJgxkfVk+P7y6guS0NO5//GnqXKk8s3wLtTuaiG3+mvDGvXy1ZiVqRweP/v0V3rnzMgZffA9by4JwjDqLvrVfc/u1V/DOsMHERYSxfM0XtGWdQcqIcYBm6bkmTgNWsqysXrcqqws7ZQorVBfms6ool6w4J9V7Q2GvStzwzrDpyIQEskcm8FTT25SNu52KnZ03xfb6vTyzfIv+/drj5sOnL2mWcpXxuhvs7EvuvNG61guaVQ3QP0v77po4jSh3KZdPHwzTB1NUncuCnFiCG9qIejFaf4ikJDhZsbGCFRsruG5MLs+v+oSYfqEoisKI9nbc2xrpnxGlW8iiP7OsYUZ4erqljuzKTCY8Pd0gW5hJ0RwGHpKcqXu/WPmmd1XtXIbVtWguPAtYhvPbWcb7Czkq0m5dd/uyit472G5w/WJiCEtIsFzX3tJAFSQrirJBWvycZ+7JX9jOdWFvXf/i6DFSVlU13fT9A+CDnmofuo6I0tJXhtPR0cGxEyaxeUsx1ZEjqN61m+D2vQC0B4fjbqqjumgjjohEQkfksg2oc6VyTmwtGeP28ezrS/lx80aeeupxlMgBKMDtN15NREQkn37yIgPHnUV9SBIkHEfWsZNp/PlHjkkNZ/d3IbRUlnPu//2Tn545XddKw9PT9TBogerCfN0NzgrypJrAJRfmUljZwJDESC4aa2u7AAAgAElEQVT4aCG1tzzLaxu9iaR2h9ZXYWUDm3c2M35yHvUrr2HEhBSQJv6gk9wEKXZalWt1wlR3lXOOIlufeeS+Xk3KMbkUf/IemVPmUFbVRGlBEQD/cGVx1LgJbA8JoaaqnBe2V3DZkBSmHRGr9yn6syNjAdl6FwQtxuVrgtBqnU7Onu9BNjmh7SC2EftZkaBMzL7a7CnrsqsAkIPR5yFEuS9N2Q/UAXpCHM9cVx9g94EOrCfRa8KsrWD39A4KCuLhhx/mhbff58N3F0P119qKsBhod0P0YHDGEzFS812OjneSlxnK668s46nX3uU3p87mvvvuIyzMmNBo3vyLeP/993F8+R5M1PJKhPSLJHrIWF4vqKLFvQ/QCHfg5bD9wfG0FqymcuVaOMmYIAjQXNf+ONNr8swKk2aM4lqPTPHTuacR9cb7enTfio0V1O5oIjreyQmjk3jmqk6f7KQIB01BwUQtfJ6UjmZGlmzgk7m3saWolvzaFrbc+E+mZkWzqkizeC/x7FeyfCuwlQyJNFPueFxzj7vtJn1Z5pQ5RMc7KS0o0h8w3yckMHzm73VPjpOLvuXvD9/DvzaVcf24TAYWlxt0ZQFZdxYQRGyWLlyZyXqUoHCV82fK2kzWXWnPduvlnBt20oQcldjV5J8Z+zNJZ5Vrw6rN7hK3eb+etPJ/YfT4XNfBQK8mZV9O/gTBZeecwWXnnMEzf3+BjEFHcsyx4/ly8/esXfc5Kz9YQkj1l0SOnM7r5w8juKGSxqZmEuLjufCKa+jb1+HVl6ru5eftpeT9/h5+dneuL6tqouyr1YbtqwvziTpvCzUf3EzUcfO5L/cq2+NofvFsj2ucNRwR0czOTsLZ0UxQyQZGLryRn2wqnKzYWAHAtRNS9Rn6Co/DT1KEg7SMMYy+Ygr9P/6G3SV1uqTwzvfrAHjw0XVE95VdrLaSMX0QaTfcwhvFe9m0bJmhv+h4J7U7mnQ/a4CGqipWbHRSmOBkSGIkWXFZ/Olvr7Mj/0P+9JeHOT6kD79NNBKygEEv9hB0eHq69mAzLReThFbeGAJWk4Vdwd880LIUInvxCGvZKvtdd3NM+4uuXNl6Av8DUXwHda6rp9CrSNkui5sZ5ovnrLkX6v+fPG4E40YMY+wxR3PfjVeQGV7LxHEfsuWT95k/fAAFP/Tjz3+6kXsefpygoCBTjoJQxk2YRNqenxh59FSKqpsorGygtKDIK1oOtEk755SbvJYD/Obay7l75lBSvlzE2qF5jM3rawjQEMicMoezTxpMXmYo29wOjrpoMY6IaHY/BjFZuSRFaDd5YWUDmwqqaKiqYvEObVwCV07O0F2ngoo2EJ6ezojXNBe3Cx5aRsNXW/h6x2bWDh1Hfm2L7s8McPxHL1Iflckbnux1My86W1/39BnD2byzmafWlBAdP1NfnpLQGWYtxpHqimX6nDymjMjkjPMvo3/7Xn6LUVMWE4FmyOvM1rWwoCtXeuQWE0GHJGcaiNlXuSrwJk3Z2pX3tar76DRp2758na2kBrviA/6kj7ULPDGTdVf3j5wh0M6al+/D7kQRHgooijIPuF1V1ayDMdd1MNBrykGZ4W8tPLsS8qdNHsNLf/sro0dmEx0bx9rCMvqmZDIkcyAffrScrVt+tGxv7rnn8uaLz3KM083ULE0jla1EfzA2by5PnzGc6MULdf9gmcgEXnhhIdkjE3RiE/qxu7GWjowxuOqKSXO4mTs6mSGJxnT+8qSeKJm1eWczHRljCEnOZJvbwdJNFVQX5uNurOWoGddy9VGzuHHHZv76wR+56boJ3PPEWbRHJhrOYVlVE0MSI7mv4FHUV/5MUoSD2dlJnDA6iXvPyubes7L1sYgxFFY28NSaEj7cWktj0mj+8dLf+GdRBe8XVxjGbCbc3UXVlH78jS1ZCwjSri8up7643MuLxApt5cX6xx9YbWfOo2xVnsoXZONBlMvyVeqpKci7XJPsgSQCZfwdgx2Z+hP12N1alL8kVFVNFz7KqqouUlU1S1r3gaqqA1VVdaqqOl9V1QMrDX8Q0GssZTk/MRif+HbWgHkfc3s5gxIZe8zRtLW1seCOu1CC++CKiuKDfy/HGX+EVyavmpZ2+g87lkmzz+D6669jwd0PMjs7ibKZM3XfXYG4ITk0lG/xsqDH5s3lg9/nEFm0moKPvyHybAdPrfmBu2cOZZnkzQBw5XWP6t+Pr5rLyxeNhftv0rTljmbqozKpaWnXw6bPPmkwhZUJDEmMNFjKALe+vYnSgiImzRjFy7HFZMR6E7kgZwBHRBLZ8SfzXyDly0WcCbyTdBrr33qN0oIcsv74oJalrtGtZ6sTlVFEiS5At94jExIoq2qiaHQSU7Oyef2FJ5h97mWclpkEdHp9wFoDCYsAmP4ZUdr3omqDHu3LzU7+5c0ThjLMpa3sPDr0NkpLcU2c5qUpm5NamSG3KxNiUMkGwxiE1W0uL2W+HoWVanbJk/OEm6//rvyQxXq7VLWib7v2Ajhw9BpSButcAr7gb7rNS845nezskQyIjyc5JYWQkBDb1IqKojArbx4uRx8W3nQtj774BieMTqJ2Ry4NVVW61VxdmE/mlDk0VFXpE2Bj8+Zy71nZ+nGMeuoRcl9cz/q3XmPZi9FdHk9Fo5ub21YSUpRpmeksK87J1KxYRgzQfLbNnhmRCRph3880Clc2sOzFV2z7cjfWsv6t1zieuXzw+/lEFq2maGen9b10U4WB1GUiTnWFUlTd5OWCZ8CgiewF+qbGExbSx9LtTY5INEcnymRttrAF/LGYwShl2Gm+5olGUVnFl0bcla80eFzx6CwQYFjeRc1CXxC5Qpye6+RA9eBfwiUuAA29hpR9zRzLVZzFX0Mcv4W1LNIqAjj79WNcjr3fsBXOmnshW7/9hmcffYiv42ZQ9tVqIpMHGyxd4S5WXah9r93RxFNrSmByBiOycnls3XZdR7bSpLM9FrjAqqJdDPcEWkR5XLKcDiDFRU6KC4A0h5vgumIid5WzIGeMnptjalYsq4p2Wbra+cKmZcs4BUhJiGZIovZgEVJLYWUDZVVNpCQ4KaxsINUVyvhYiAkLZ3u9kxUW7RVWNugEfkTmICY9toQRqfHcOTwV9eNv6J8VR/+suE4LOCPKQMiC6MUYzBayDOG5YQUrkrSLDJTJ1aovq9d8JTbZUKFF3tZsWQsL2pelHdxQSZqsRVskzxfWtew5ItcK7A721785gANHryFlMywvkjBjSSe7BCv7i5iwYCoaO1/Tb739Dk49eTrZfzqd0uTBRCYk6AQsIHtlaGWr5rA9O4mn1pRY5oIWiBuSw+XTB1M0Okn3TZ6aFUv9R5rbmLCElNhk0uSb3N15Q7eveYswIGNkLjEDMrn8ye5p3wLC73lIYiQneMYD6ONf7xnvlZMzCG6oRHs89NcfKO7GWhrKo2lIHkxKwii93fteWExOjMr9N1zBNZ99y0M5Qw2kt7ukzouQhX81nofB7qJqrwAUO5I2u9yZq88I6UJeJkjVLgmSXa1HX/ArFNsiEKXz3FpPwm1za9VbzEmY7Kqq+HLLs7KMD7a1HBkdTkS899wKQNuew1fD7mn0GlL2x/PCSkOWidnX/rIPplVaRQFByACxsbH0C3Vw9qBQFEUjm+JPjO2aLeCyr1azdGSCLSE7IrQ6g3NHJ2uJZjJDaXz1BVwDplH/7xeox0gQrQWrdW8AuaKKuqtcJ47WgtVsHppoGdrtiIi2tNJlaPtpE46zszUN+PmXvV0At9fPpLVCW75011hDuyK96KaEBN0bBEBR3Cy4+hJiX3mTq1d8yfPHjODI5AGEp6fjykw2JFdKSXCSArp1Lpbj2UbA7ANttpjtKpmAd4FWs/eGXcCKnQ5tF+Itw0zIOvGJ8lg2mrEcKi7cH8HhFX5tHpPcl11qWrlfATvrOSBr9Cx6DSlDp3O8DLtcAeaJCZlYfTnXd17cGmQStsKw9CQayksYkjjUi6isEJk82KfWOvOis1mxsUILZa6r5NVRZ7OlqZUzpn/jlcTHSoeFzldufeJoch4jgsL1bG4i93LKMZqOWfbVaktiFvmZG8q3eHTxOVw5OYNZg6IprBzFEikaMW5IDjkpLoIz8/h8F7CmxPL4yr5azVNrNGIeMSCcJsIhK5ff3TmGpOw3mffI01wx92yOHDmGwSdNZeSpbXR0dLB46XK2V1Sxq3Y3NTt2kNQvhKTSKo6o2QcmvbmsqgmRMFWkCZXRVl6sF3uVX/W7cpcTMBN6a8FqQw1C88NR/BZyBW5/PBxkyBqzPH4Zwu1RSBv+WPDdSTlqtpQDZHxw0KtIGbxzyFplvZJnpMWFY+fHKV9YVlKHSI9oBVVVIcRB9Y4qCluSbUOmZUyaMYpNBdbJiDKnzNEt6PMTLuf1kbW6dSgI2RA4kZ5umCCSX0kFcfcdmau91obByxeNpaIxm1VFuXqQCUD2RWd7We4yIQvCLv7kPbhqAs6OZu4reJQr33mcp9aU6An1a1raOX9JiW7JZk6Zo1caF3A31rKpoIqn0PynBZIiHBx31sU8mzGUtz9czrtP/p2K0mI6OjqIi4+nrV1l/NQZRA45kuFT4mgqLyK/ZS2P/7yRlOgIjk2LZ5wzFOfXO0lJcLJ5XRkpJXX0z/COHATfuZTleoKAl7Vshljva+Kvfu3KTv9prKMQBVlbyQRWDw0rjwi5CrkZZqnFyuuiO9F6h7NbXG9GryNlAZmcu8qJIWBHyOaLKynCoVvL4n8rYl6z+lO2lVdy/rTZ/OuDrX6Nu6yqiYaqKq+8F46IaG49/2iu9Fits7OTKLh9AaOvmAJAqWcSTEtwpKEjYwx2t4V4ZQ+enEfFLi2qT56Ey5o+mKWbKnSLdVOBkUAjPRo5oBOzSKLUvuYtHnx0HTw6hpc/+CMhSiZ3rUvnkXue1/cv9VRAaajyzu+h6ey5XsRc09LOMaNHceQxEzp/k+Y6Nn7/I2HxaURG9QfE28uJnHz+FbhCVPI3bubLtWt48pP/sKuPmzFKBxOGR5E+Op0gRbHOteF5qAnyNZOuTICGBPvgRb5ivblqt5yHQ69PaJE7Ws7JIROzgPDQEPsLWAWf+IJd3UmB7uZ5Nk+wB9Az6LWkvD+wuujsLihBwrKUISAuxiOinShKEI7QMK9t7CBCkiOTBxM3JEfP+CbIMXvmTGp3NHFu3G6+AZ2E+xeXkzhtopclZuWVosQmQ3kx4enpfL5LyxwnfIlrWtr15Eizs5NIinBQ09LOM1dN4HLwsmyhk6CzRyawqmgXSePmcdN1a3nw0XVcf8pDPF63gcWL1hkkkOrCfJgxynLy091YS0NVFWXxTlYV7WLuaGvrDYDwKOKP7JwclB+YAPn1eylqiyF43Bzmz5rLEWo93733d15f+h9WfF3EfadqeUAEMcs+0NrEn++UoaIwgYDu/2xTMcVsZYvfS67kbecDHQ6WsoSAL7c6K1hNQgpCtvJ5hm7M3XgQkDB6Hr2KlM0W6/6WW+8KsgWwvX6vvlz0LS7o1GGj2bmrhqTgFlISnKz3o+3skQlAp5ZbXZhP8Sew5DHNWq57/QIgjm+uvIH0GaN0K8rOFUvMxsseA61yUn3T9jjSdZe0VFeonns5K85J9sgEGqq0oBdhJYu/0fFONhVUsezFxTyfPJhJM67j5OiN5Ne2cE3UGD68bgJp/7qFN6r766S/+CP/vD3k31Wc24pGt+Hci/FCJzGvKtplmHxMdYUyYkA0k04/ifPHZPDAO6s47/WVPHLqeAbFRentyAEnnX7H6V7jMmvR8jYyQZrlDSFVWOXh6OvJlWGGvH1XnhEC/ljG9abJQv1/09yMr8lweTyWk302AVoB7B96bZh1dwi5pqVd/1h9t2rXPOEH3lZzXSuMGXkU33/yL50YfEG4jWnkV+U1ueZurKU9MpHbsuYw7LI5uCZO0zOkCULQ3d08N0lrwWrq3l/kFTL85dA8nlpTovlFoxFZa8Fq0hxaBF6qK5SkCIeev2PpJk1jjkxIIOWYXLJHJhBtck8SE4LVhflsKqhi4Jdr9HUPPrqO36eewtpjJnHBRwuZmhWrBdN4pJq4ITk4IuwDZKzeSACKqpv0jyDpmLBgkiIcup80QE6Ki/GxmqaqxCbT7+jjWXjvXVx3xcX8bvFqlhdu19s0B6KY0VxaSuXKtcYK3BakrcQme1miotxXc2mp4fdQYpMJSc6ktWC1/hv6m7/ZiqCt9hVh32J7eT8rMu2JNJ+He+6L3oheYymL8jfmCTu7wqVW5Cpe1e20MJno5XI7vsY08Ihkvt/ZzDebKnxu64iI9qTyfItVidMMngsCt9x/E3cMOJbBzr6eBDvaDZ44baI+oddasBqkWn+uzGSUC+7kHU/CIFQoCklnxdub9HaLEpu0YqilpVD6AJM85aJww4KcRJqCUnF2NHNP/i4++/gbT1Rirseq11C7o4mUY3L1IJnoeCfb6/dy447NWsBKQyW3Zc2htrVd15tfGRbH6CumoFxwJxNvX6Fb3YDedlackxEDxI3t9vo9zeHiOSkuff3s7CSKEjVSzi+rhxQXMZ7Qc/H7zMlLZPDANC698f+YkxLDjGDN2tYjAk2TgLL+68uKliEHigiJxJWpkbCBUCMT9e3stGw7dGSM8crTbDVZKbvTueqKcdokPxLQl4fZz8vILnM9naBfxoCYMGIscsAAuJt+Pbp1r7KUhWubuOmsCFkQsJ0nha+2rZAU4TBIJqINsf1/128ibsgxPt3c4obksPuxE3h11Nlcf8pD3Dag1Gub31x7ORd8tFD/XrJ8qyHxvHxzf//sezohR502j807mw0WpexZISPKQ8Z17y/SLbb2NW/x07mnsXrCySzIidUTFAmYEyVlz5xJZEICpQVFLN1UQX5ZPZ/vgs9aE7nkymMN277zfTULrn6bWyOHsXahVqEnOt7JrecfzZDESEOYdlDJBoJKNuCqK9YtYSvIoeOprlBDeDcYf/ealnaagsIZNi6Xt/7+GK+X1jD41quY+MCFlFU1acEpksYsyw3h6ekkTpvYJSELCGJ0ZSZ7Eb0gsaagcD0hFBgrdPsDcQ2IvmRCt7KmRZSgq67Yp9QR3FDZpeUsQ2zrq1J4APuPXmMpy1qjBs3Ny7isU/eVo/pki9dQ+bcLLUz2b7YiiYLCYsrKyumISqR2h733xRt3n07Bdecw8YcvOTUsmE9yjoO+I/T1cUNyeDl2PS0Ln+e2aS/x1gVPauHForbd7Q8bEvAMu2yOnl2uPigcPMnus+KcFFVr7mhygIUgrvbIRH3icPuiRcBaVi/aqBdTNWZf0CL4Vmys0NzyPG3KyeyXFOaDJwVphruU3z+6zvYc/Cv9GApK1hgepEJ7/nBrLbMG5RJZpEkxronTcGaMocIVakiuJMsVc0cnM2JAOCMG+P4NhWV38w8umpwDyZ6/gMkTJ/DIHbNYeseHZEwf1GkRS/vJXhL+1PxrLdB81GUSFxqyU55cCwvHZZr8C5e27yqfhlXJKfPx2u0nw6oNszeSfK8EZIpfDr2GlLsDO6tXywvROWnR3VlkcxBKXHwCk2fM5qErf8vPQcMg2NoLY1LfStSFN3Lci+u5fPpgfiiqhWGd67c/OB51VzllLe2kYLROd5fUUVbVRN5lcwBInZdJQ1auXnUEtHzGZVVNMLpT107RE8w7df14mxtInkhMWDApd4whqGQDKcu3Qm2LKbG9NVISnGxaZpy8K6tqIiYsmG33+U5Lm1/bwoi55zBy4Y3UJE/UCVkvkzVI05vri8sp/fhh0meMYvz5fwJiyYpzsnRTheFtRESvCekE8JrUEstrHOmUVTWxL+MUxhybQun7j/BFZjYpCU7vzHI+LGNh0bpMpOxLG9YrlER17iOHUgeVbOj0c+4iaMWM7m4vQyZ3f6ugWHn6dCf4JAD/0GtIuZNovS3hGD8sXgA6jD6aXjPSHpgtBuEdYJ6hTurfj+xxE1i5fAWqU3aEMqLEkQ7J6ax/61pKC3K4ALjom1WcU7SBSX0rqY/KJKxgNQ23P8xaTzUQGXNX/JW1kVq2r6QIB/lbaw1Z2kR6zBXACaOTDN4I+j5l9fo+gqiJHEPuun+Tccc1lCzfSvuat/jNtZezqaCKW88/Wuvck+tCTwTkqcsHmk5eu6OJzTubGTdtIjkeq1sQvJwsPyc6jGGXzaEjYwwVu9wGrXhIYiQ1Le1Eosk273xfTc66MuZOnEZS8kS21+9lSGIks7OTSHWFsr1+L6uKdpEV56TCFUpSRDppDjeuumJjLgs0LRY3XD59MEsTnFoFl6RLePLeu7g7I8XrtxLEK/yRrWBHoFbpQYX160LTZpsw5kR2SeHcZojtxHEJa12cVXkffwlWhlXla5CqZHsiA72kwC6iagM4MPQqTRk0QkxzaHKFrG31xIVhTiKuk6+NG15DeQkvPHw3e2LGogbZP9/+b9kPTLn2TUDz3x36+acAvJk1hrr3F+HsaKa5tJS/L92qa50CU167y5CmU0g15lzIMlJdoXrWOD301gbb3A5S7niciQ9cSHNpKU+fMZx/3XK8TugCpQVFeh1AuRJ3dLzTS9qZmhXNYGdf/fuZw+LIe+Uqr/SUZsu3I2MMEx+4kDOHxbGlqZXtixYRExasj0VzeQu31JKhk2QEeXVkaFnyRBuzs5PISXFxUkYkpQ3N7Gpt82pDhhwUIuCvxiwTdFt5Ma0Fqwkq2eB1nbZHJupeHOJjDu6QozQrV6710qD98eLoSvuV+xOJ8gN68aFBr7GUrSL4/CkEKWBnFQvYyRd2hNavfQ+X3raQQVPmULapBdrsk/p89vE3hqi2iy++ne92bKbh4tNZcPXb5Px5qa7r5te2QG0LfF/NPU+cxZvqcCjeS06KS/fNFXmThb/uivgKIIGUBKdOVoK8KxrdxISF6yQtpAyxTtTwG3HcfKJGFtP66UtEl5aSkp7OiOPms9kVyoqNFTSUb9E+yYNJH5lFZEKCXqgVNKstJcFJdFOrluM4IwrWlTFiQoqugdd7dEkrfV5zdwtl/OQ8Jj4AGZ6afFrYcKK+TVKEgxEDwr3aEJpnR8YY+loQmzzXENKnDxMSY1hVvJNBpmraZn9weTJOjqYEDLkowDvxkW51S0QuEgDJGq0cTGK2XmUdWq46Lvo1Rwh6VdKW03hihDmhka+iq1YIWMgHB72GlAWsIo66U1p9f/LEWuXWeOPtd/l5Zy31SUcTmVztM++F3brIF/7JPYsX8sGfl/K72YO8cgPXnn07RRvLdaJNinDo0oNIZA/o6TSF9SwISFQkGTFAsxYFMYt1RdVNekkr0DTZmnHpMA4+GDqO3HlrGbHweU1LNh3LpBmjdCkkzeFGiU0mY/ogVhV9oevgp9w5m9KPv6Fy5VpSkzP1V/W0jDG2lv42t4OMkbmwUqtA4np/ESMuuJPt9dpxC13fKrhk1qBonB3NmlzkBmj3krnEb5mbEsur5Z2Ts7IXhlUSI/B2X/NX0zX7metVQSTIdf3k/Bfyta71t9arTV/oKlG+VT9m9HQK3AB8o1eRsqzt+hM8Ik/4+AovFfKHL8IObqikoWkPD7ywmC0l2/ju++9xnXIL5V+sJTJ5cLeOY2zeXJ0wc86+nTxPYqH6KONNXuGZzCuqbiInxaXJNh5ilYlpalasTq5CrjC7honzJfaRJ87mjk7WK5WIKLzNK/7KVUdfzpnLj2P2q0tYRmcaUhFCnRXnJC8zVD+/qfPmcQmwetFG8l65iuDJeYycuIHXTrieLU99wSVXHkvaDbfofeabqpUItEcm4spM5vmnvoDlJdwInHvaPNojE9kmHbeYJBQPpJoUFzU4TK6PnZOB6q5y6teuBGDQnnZK97VR524jyhFicGOrL7aWA+R8zOHp6QdUKbsrFzXA0sdY7t9XWlC5irY5tNtchVtc+3I/ViHaRs+ngxNNG0AvI2Wzu5sZMrEKH0oR4WSXJ9lcb8wuNaELuPe5NymvbWRa3nzUHX04Y9Jwlh41BMAyGMQXBAmlOdwEmYMM8A7x7hyPMRhGwJirw2HoQ0gUct/CUhWW7ja3ZoWLKL431cHcdN0EHnx0HX+teJ9npDqEcUNy9MxwQUWrafdot2TlErUwl3PvaKZ9zVuGsd/27g18OTSPn1the/FewHhsAiL1ZHh6OpdceSyuzGQizv8TJW4HFbvcbK+vN2wvCDkrzun1MBLnL81zmpTYZFwTp6HEJpNQXM7w70pZvvEnTozVEh1ZZZOTkxmZ6wNaWarmZXb6r+xuJ3+Xl2FKTCTLJL6sdFFVx+ouafcsl0lXlgPt3hxl11KrN9WeQHpsP5Jt3qCaG349lnqvIeU0h9uyFI6AWV/2lT/AHOHnT+z+bsXJu+9/wN+WrOTljdWsfGcx6z/fziUX5vossWTOBpc5RXNt216v6cRBJWu96u1ZhXhbLRdvDvIrvbxc9CMsSt3rAnTrenysdq5iPFa67I7Xfsuz/HXaIipXruW/d+Rxz+gkVmys4ITR2oSZq64YFe3cb97ZbHiIpA7NYzwakYyYkMKXQ/O4/EnNjzl7ZGdxV1nGEPJKcEMlQcmZpM7T9NISt0OXW+R9hAeHsNiDilZDVi4VjRjGIpf+wvM37YZbmLn9et5cu5UxDYruF25OkSr+ry8u18tUyVKHFTHLwSHh6elUevTxcLyzxIlk+rI+bc6BIedSFoRsJUfYZX+z2s5M2MJA8UXoVulvAwmJeh69hpRlyK9gdsRr5fomyMs8SeSPHBIeFkbbvn2sq2jms4+/0XNA3HezbwtZJJQHzZ3s7JMGkxXnJCfFxcBy7Wa1s1DMHhCiHJUM2TI2QxynSDhktc4jvrJ5Z7PuoSD6rmh0UzE0j/GT82gKCtclEmGVquUaUZiDQbLitBDs8bHa+PtnxREZ4SA63qn7TwvIgSFinKmuRCbFdoaUD0wup8I1xlCMdXZ2Z1kq+YP9yhMAACAASURBVDy56opJikg3PIxGDAj3eki3RyZy9j138+CJZ/HS9mqOq2rkmAlH6OvNpaME5PqB4K3tmhMVASRO814nw1yCSiyzghUhm6UGfXLQYp15P7nqtdn/WPfAMMlq3ZlgD6D76DUuceYnsl2ybrt9hW6sTfh4E7CviYwf1n/G6XPnExTch7eXfe9zUs8q6Y67sZbsmTO59fyjuXZCKrMGRZPh1hLWdPioNixSbuZIOrJsAYuP8KIQy81ucHJWOLCvpiKsd9GfaP+t4r36+ZHJXU6CJFumggybgsIJSc4kcZoWsCJ8qOU2yqo0ot1UUMXij7boiYeEvv79s+/x2gnXc8ST15AV59TrBZrH3RQUrpOb7EYnzht467jhiVk88OC9NGb1496+jeRv2WFYX19crn9As5jlMGqxTkgUgnRl9zYBfycFfW3nTxtW17FVEVZ5nQhvt0OAfH9Z9CpLWXfbMeWjtSvo6KvKiFl/lElMWM5i/6CgIMp+/pmkCx/nq/f+6XOMNyy4RJczBEHPvOhs3UdWjEvdVa6HSsvjlccoAiUEWZoDaGQIohV/OyP+vMlYfg0NKtlAa3kxI46bbyBrOYRZG5fRSv9way15k/Mou+MaBiZnEjMol82e8crn0elxUetbV8zc0emdyaBSXLzm2a6sqkkPSFkRP5es6YNxdjTTjmaZ5mbFkXbDLayt1vyihyRGGnTxVFeocTKsrpgRAzKBTrfBmAFagp6gkg0GuSh72hwenjaHJ+fP5rv1ZeR4rOD0GVoOZyuLWdaZxTbQGS4tB51YTQZ25REhJywK2lVuqE5ifjs0P2jk69pKpjC7zFm508mwk0TEnI3TT8nkl4KiKDOAJ4Ek4EPgYlVVG03bfArIiVreUFX1ol9ulL7Ra0jZygKwy/HqL7qSLJqCwlmxei1/feBe+h+RwRdL3gHF/uVC6MUyvn7nz4bvNS3tOB2dgQ1gnJQU+WljwsL1REOAwZ0NvKujyMcjCFlY2lYBJIKQhTeCK1nTYzvJXPNaaHz1AdrT03GNzNVlAYFtbgcD582jfu1KXMD4jDEkRbgM2jY4wKGRcUZdMWp5OdsXLSJl2kQWTM7jnnwM5bHMlrArU/PPbY9MhOq9ui92UoRDl1NGDAgHDykLa9kZmahHMmrnMJZJnniW4AYtirKmpZ38snpSXaG40gbSXLiTzR7f6u5AyBk6gZv8k835loXkYRcxaGURd6ditvm3lkOq5ZqBVvKfTMxdEbL8/XCAoij9gTeBi4BPgeeAu4DrTJtmAC5VVe0zlB1C9BpS9qfGni90tZ2ceCWqL7z2wX/419tvUrL1RyJzfkt1UxRUfeGzjbNP0lzjRKjy2oUn4Oxo1gqJopGHq65YyLjINFtvSDlpbFd+3ZeDaGLCOj1KRJCIDJmQZSvcnBhHPgeC7PPL6skH8s7/E42vPqARRXI60ClPpLpCScsYox+HmDAUFrWw8oVWnJeZSNAuLRVpsyeN6ILz/0RWnJN7TedQQExsdYAhAEaQqR2agsLJL6s1uP6Nz0nUyy2J85aXGYq69Uvu+XA11LTRt60vrCvzcpGzs5bNk4N2WrSAkDr0CiYmyJqxuUKJVcUS89yKOe+HIFahL5sJ2U4f9peQDycrGTgV+FpV1fcAFEW5H81a1klZUZRQYN/hSsjQi0hZ4GBMMsiWalhbI1NmnkZIeARJx05nzzEXUlfTCnusi50KOCKi9Ymn2dlJPH3GcIkI7RO4BJVs0P6JyvTKsQGYIvQ66wXqN4dE5na6uOxhIrwkclJcOpmWfvwNI03RagJNQeGEp6drIc8LzXnkNGs5TdLFZS8M2Y8YYFuKi7SMMVouCEE+DZXkZSaSc8vxklWrwWwxigAY4eMtW+0jBkiylrRPWVWTbn0LcpKxb/U/mHXBg9QGdTBuTzCDozVzevWijaQkOL3ykIDRM8PsRucPIQvitqpsYiZkO1j5IAsElWxAxdq6tvPcEPDVr3i7EjlF7GpcHkJkAxul798C8YqiOFVVFRdWOtBXUZSvPf9/ClyhqqrvG/wXxEElZSt9B9gDvAbMAO5VVfXhnuqvq6g+X8m5dSskOJjm5mbueuxFNtYFUb6xAmjtsm93Yy2fffwNkzxa5OadIpy4XY+++3BrrR6NBx7tOktLWSmHH8vRarI1KMsCaQ6jdW03eScITLSlZ5SbPpgMD58IYgluqCQtMhFw6P3XtLSTMTKX3c++R+KnLzHruPnUeEK+RWKgzpDvZr1EkwxhqVZkxWpRdRlj2OaWCtK6NcKdNSiaGo92HlxXDJJXgl76KipT19pFtRRNW47tfCvwyECprlCDi19NSzsuNNIa6Fn2xzv+TqsziOk7+zBjWIxOwnL+EXOlErmclBXMbnKizp8IwzZr0kKiEQ8hX5qxGWZrN7ihUi8HJgqxigeRr6g+0Zfct1fxVs9vIQj5EJBxsqIoG6Tvz6mq+pz0PRLYJr6oqupWFGWfZ7kg5f7A98C1QBnwOPAqcOLBHHh3cNBI2Ye+82/gB+ASYLGiKM9KT7FDjpaWFhRFoXnPHiBCX+7L42J/IPyKKxohKXmiLmkICJ9ckRlNQOjE5pvCruK2FWp3NPHM8i0wfTDnnjYP3l+krxMShOgzJiyY+rBMRi68kbbyYiKLVmuyh6QvC5lCQM8oZ1NFQoYgdzGRach3jbcfrwu8tG15DPKkZlKE9tayFGPS//q1K6kvLmdx/hY+3bmbi5vDKFf32Y5RJmqRp1q2oGUJw1/IUoddMIgvQrYj2PbIRF3Dbisv1gu8WhVQBW+5wwpinZ0Lak9F96VEhZFpkWgKoDGkDaBcVdXpPpqoA3TxT1GUvmgct1ssU1X1cyQCVhTlD8AORVHCVFVtOaAD6CEcTEvZTt/5CO0B3gdQwDC53CV0IrJInmLl72vnA2xnNT/+wmuMHXcsY4cP4ceN5dTuaLKs8GyFSy7MNeQv1gmmw5MO0UM8zo5maiRr1BB04SEVUT2kKLFJ11LNbm9imdxOTorLMLEnW+Vy4EntjiaWbqogdXIGk06bZygz5Zo4jfEma0h4UQiCTMtIpMJC0y2sbNDlAtkn2ZzrQvhcryra5XXsSREOMvD2sRUkkzESYgZ1Wsyi4gpgkD+mZsWSk+Iy/B5qeTmlH3/D5nVl1Ae1sTu4nY+aW3B2KLxWuJOEohqCUBjs7KuRr5SPpH9xOSzfqj90rGr9yfKFOYjELFckTpto2A6MiYPsvDTkiTvhAQGd17ooTyUKuMr7WcEq9FpeZ97/EFnJ/qAQjXcEhgJbZbJVFGUm0Kqq6grPohBgH/68Dv9COJikbKnvAOuA+cBPwEJVVfdY7awoyqXApQcygO5MAjo7mikrL+etN15j8ZJ/+SwdZYWxeXMNhJzmcEOH2Q2v63bERJuo9JEV5/QaiwjssIN5Yk8sAwdDEiPZVFDlVRQVoNKTBCgdT/7fjDHUiDwHnlzUIiJum9vhFfIsQ25fhEEDhuT8ZsgPp4y+na5hbeXFelSc0GJdE6cxIkvTuGUiFpAnR2XXvrbyYvpnxXHKjFGcAmSv3cxrG0v4aY+bBrWDJnUfoYpCtKMD1759pK//kXsykug3cKBXTgyz1GEV6WeXH8NXZJ4ZVgEgZmLuiWTzMjH3UrwHPKQoymQ0vrkXeMO0TTRwh6Ios4EK4AHgHVVVD5unzMEkZTt9x6mqap79bvr2z6FJHiiKolvTZi8Mc8inFRH7W8pmyeI3mTP9OEKiBrDdo8Vmj0yg+JPObVo3vghA39FGt8YTRncmYE+KcEiTXrWGdJli/CK5kKz5ypNYV07O8LKkRRVqc+i0gMiiJo7VnE0tJ8XF3NHJ+n66vl2nTULJrl2wkvDyYgZazNRrx2UkZLPLHmCQXoT+DMZ8FWL8Rn0Y6gdlEsZq6teuZOPTn7CqqJYzpmfoUkF4ejHOjDEewo019Cufa2dHM8F1nXpoO5A6bx4NHkKfcQGc0aHl6mguLaVDValrcVO7Zy/bt5bxhzWbeSDlCEKSM3Ec28yA1n303669DVtJFjIxI0X7+VuHT4ZVpB9Ik90mYha/gPCykIlftsDNlrDslSFb53Lf8kNBuG36mxjsl4KqqjWKolwALALigLeB+xRFmQfcrqpqlqqqrymKciSwGu1NfTlw1SEbtAUOJil3qe/sL/bH88Ifq3ll/kbG5l3O/y37gSsnZwAaeS3pYr+4ITmGqDn5QpVfnUXRV6dDG09Fo3eAh1ETNWc8M8Iq0buYYNT0WRCTdrIuLYgZtHPZHplI1GnzGCblaeiMZFurV9N2oU0uylGFgJ7Briko3BDUIbvO2UE8ZGR5o6i6iceqm5g6NI+JyRvIKC6nrGoj7y4vYWpWtG6ZpiZn4swYY6ml64QskQ54yLG0lChT5rRGD2k6Bw7ECaQA2WNHcdsXP9A+eCyfle3mirteYa/bzejMFHJiwxhV10Smqx8gVcf2WM92HhbQPSsZOt8YhDufWQeWydNXLUFfIdd6P559zK52VnKfnZvqoYSqqh8AH5gWL/J8xDa3Abf9kuPqDg4mKXep73QH5lJMZhyIv2RtbQ3PvPICxdt+pmSjSvk3i4GzuXvm/7N37uFRldf+/+xEMklMMpgEyO2XxGTggDWJUWyDkICAooC3Wom1gNdaRCvWtirer3g7vdgqUI9XpPZA7VFPkQpFqgiaihoTbI2QpJHmhgmRTGJgosn8/tiz3nn3nj25YLDklO/z8JCZ2bP3u/fsvd71rvVd3zUBCBUVsuOKS4JUMTEQe/f3WCviiFXJOInJ2ZNVEFxqS9lrVnIqn/iCRqexwxfWyAnzQaoHJXHm5MXKdewUtuzIXOKKU8lMzw00VQ2i6bVtoIUPki6+C6eqQv3htDMwZMxOoQYZnyQ3JWZb1eQlbfZkcq5Pp4T7YVU5L/y9JdBuahfuXLNoJSsQ/5bQiNAK42xDlPAMmPFhO31NL/YQxLhcrHplCyufXU1n5tl4O7qYcP4k9r23jtu3vsPnnx+gwIhiXAdMGm2ex8Ek/nTYjaxuKCM0w2oX2Rfo4x+o4deTgyPScy3l/+az1xNMJg9SDP8IBodDaZQHEt85aNhn7v70kJ1mennvf1Y/w9bt5dy14jk+8h3NprSRylPO8dUxZdYJvFhV5hi6sFfx6TKZ8r/QxeRz0aPVjWvQKzbj0bWubACy8PXZVdtu2HWP094BvC92hpNmrw6L6E71FuJyJqriFfGSTW2O0HHYIaELMdDiycv10GVFGzt8EJ9NzvVLOSt3FQS6tLzw9xZY8To5NQ2kzqiBqZdaVgRZLh8Rte+yL1CxWLfhAwCVuFNFKQHjI15nd8UWi5FzjTiKNWvX8tvfPs9HvYmsq2zkjvNyiDzzOO4BanfX88q69byw4XU2RvYyJ+4oCvZ2QXVLv9fR7rk6eby6noZ8bu+uLdv15kwMWyqtJw/F49ZZFQBRGkfaSZJgoOyeI/hqOGRGOVx851AcyylmPJiZPGPsBKJ27OQj39F4RsWx5OoiXtnVxubqVpackstz53URdUl4g1zV5A0JJTh5wfYwgs6M0GPLWITaXSTFhFLe9L/1qjkIJgLF0Otj0T15cF552Hm29iozw9tERO27JGAmA8Ugy5il/FlaV+nnLJ2p7Z60jHu6J9nxuiSNziV+wY1ctOBGSt9cw99/8xI73qqHjbv4rLqFPE1HJMvlo/7Oa9myqpydnd2Mi4tSpdN2gyyrkKSRucT1dllKorvq6rj1yu8xcfIURiUZnND6IRfNSKc9IpYd3anmdY05hsIrTmbB0gfYUVnBn156geu3vUA6kZzW+hnfu6CY2KijQq6rPYarv69fdwgaTTHM4QxyZ0SsUnTTC6Lkd9MnnhHpuapVlHwu0NkVeg7EqXBrqJKMRxDEIS0eCRPfOSg4aTc4CdJbdGFtffnClY3+x3+M55OahynAXEJfVdnI1g0f4G3YCbdcwabyRravWR3yXZ1hYG/RpMNuTPV4Z09CKjs6sGgF6/33xEvRebh2OpzQvgRihCUubR+DhQvca71+7gCdSvfUwonWKOEcj7XST+9ssglYdkG++mzlxp3UVVSTXeBx5DHrnGdRyJPf/tQVlWxfs5rcaedy811Pc/bYRNz7avhg8fXU33ktpXf+yhyf10zmnWUr0gDTY86eFRAPCiQy9fPpqKtT38m481d8VnOAj+Oj+bgbSEhld8sB1r32oeIrC8anJjC/MI8b7ziBpZOz2Lj5bX5fvovNv1nHqsVnMSIydPJz6opt18nQYTfgumyoTo2T1/r/TscYAWHjxvbmD05/DzVS4lxhQ3Ptvf8+XvqwK7O23xROGeDBtos67v+Nov2zvXR3dVLb3ktlRbOKIW8qb3SUi9QxPjVBlT67faahFR0K3YMUgyzx4p6EVEdqma4MB6bxFU9YZ2To3Nz5hekhXrF4yzJZhJRoB6BrQTAyF0bmhuhi6HBiBIgWh4go1Td3UldRTUJKCsvfrFU6zXUV1XgbdlIHZKScEHLe9pizeM2PvLVbTYw1r7/EyjFxwDjgGL579495ff5tuHMfxD15hhmSSEjFDcRmB6vpRqTnwqpVlK94nZzTx+LOTacu4HHrBjbn9LFkXb+UWp+LdZW1AGr86yobqaxoxtvcTNueFDUxC32xKMPNsdn/wVmzjmLOaVO47J7H+dEv/8iTv1pi4S33ZXwHKvMZrkefUwLQCV801KiQRbicjHtfTYgXDcFn54iXPPQYdkZ5IBiI+ptu3F/dup2RI49hbMpIjsuK5vM9u/Hm5OFtaSAjxVnDF4IVayL6HrnPVB8TKpwdYhDBvJnfboXN1cEHR+fzAqwub7BQ2ASqVFobw61aOED2o3vXm6tbVdeQW4qSVdxQDHRW4PUru9oCDVVTgwJKAYRLLNlXIZ5RcVCYxqbA+OqbO1lHI+NTE3jgp7NZV3lCiNC9rhsCppc8KRl63niaqIISbilKZcmWR7Xydbh1/UdUVjRz0fXHknP6WNrm3U4bcGztNrXvEem5xAL7p17KO592MenOibhzHyR+wY0AnHBOE8dVbLEYbq+nhNrAxC60PqdwFJhFOCLer6Mr4HFPr+3mN1EHWP3MS5x/aiFg7e/nJFivw96lBAi7knHyip288ZDjhTGsfXUYGSjN9AgGj/8zRnkgXXl16DfZxx9X4YoaQcqXbUQfFc3HT93KKM9JTLlsCS8+sjLsPnQDItQyJ1aFLr8p7XY6I2KV4dZ5u/p3qpq8SnPYvk1/3ru+r93tB1SlXVWTl86ITMDaoSLS28ReV7biC4s8pnBhxViI1CdYPTV5QNPiTQqeMsy2lYb9POR4MsmQj+rK0ps8kf1TL2V/YP8xbzzN7856mKLEGBZ8sJb65k68zc20j5xJ5sKF/C4Qf84ZlW5Jou33lKjJBpKZcs5C2gNx06yEVCKLSxlZYBo0mVQlHj5nbKLjb5qQkmJ5rU8yXk8JsQ011G34ABcG8X6DtspmPtr/PrsTjuLdP2yhK2oEJROPZ2raN0gZleT4Gxq2mK/d6PZFq3OKPdu/K8dw0kW2x6SP4OvDsDfKQ0Fev3LRYlLT0rnhqsuIcrlY+P1FPP34Sl7++S8gcgBleAFIY1N7XCykC0aAUiQCPI0ea/GDGFHpyiFGrb65E8/p49RkIH3uBGLYxqcmBLzdYIfrOWMT2Tt7gslKqN6iiifAfOAjat8lJxnFOrFn2u3FBDrXVl/SZrl8JI1N5JVd5oQzszDYtklCGlsxjdq8M8axuDhH6R5LXN69r4atCRO5eUWlCoHcvOBESotL+f5cM9xgjiWKhJQUU2goOZ3M7kC83feZpeBFF/wH0/DG9XYpvjig4rFigGXMduH+8akJwQkEcyWgF8lIDiBp6qUUpOfSdctD/OODj/kT+3ni79XkHZvG5PE5HCjfycayD7j3hddISTqG4knfpHhCFicmjiAmaoRKAtq9YPkNnCQ+ZTvdIEcVlFi2cSqbFvSlg9GfoNcRDB2GnVH+KnzkcMk+gLPOPof8/AI+3L6V7Kwsnj1qBH4jvMHPnXZu2KSEPakmsEhsBgyz29dEVnKqJVGpG4FwHrGuKSEG3ClxpnOlExpMgS0jOV3pb0BA0jJgdKckm0ZLjCoZbsVMiATck2f06YFFeptIaG2gKGOyWiHIGOVcvA07SUhJoarJy5JTTK9dv5bPtxxDdUsrbXs6zWQrgdVGbrQqkfZ6Spj56W6lEaKfq7+hwTJZ6JCKy72qM3PwM5lU+4OEZOR660UywRyCC3dyOsm5o5j2bh3nzy1iypSJxLpMadCub5hsEFdmJh91RbL143/y2Iub+NvOGgqO+w8KJoxj/4h4OvZ3093dzRdfdBPR+yUxRxlcOv8i8jNCJUUFukEeqKerr4ZE9jOc0T6CQ4thZZT7Mqrhyqv17/a3j6zsbP7fyGjOPG8ed9xzPze90OhYNJI77Vy23T3TShuyVTmpY2rsBmvXaZOEb29KCag+eXZ9YTANgB4SSBxjjXnrnul0TzI7Pu1i+Zu1AXbAZMv4dIPUk2AK0INZFbjsufdJHBNHdWGaYoMkjczFTTCmKYki+zU1ktPJ8dUxvzBbXZO9+3toDHjhOl7Z1aaSZ2CWtQvyC1JIHDObjJQ45hemE1FrVhe+M6GU3bvaVEfuLJcPf0MDCXrMu6EGd4F5XhJSEejXdX5hulnyLr9XTLAqUUI44skLxFte/9RaALamjyMhJYX8ghTlNSfFRNL9zhb++nwlS07JpWDhAstqQ7WOSs+lIGciuXNiuRj4vLOT7e/8lQ/+VsXfPv2c1q4RRETGYkQeRfqYeI4Z0c2CH1xDcXExS5dcRXpMb4jo/UCrBfVu2UcwcBiGsQGzGO4TgoJqBuD3+/2houODxLAxyoOdpcNR5sIVmgB8+eWXXHfrvRw/aSocewLZBbGORnnlNaeEMDwk8REuAdLXZOCEpJhISnOj+URjYDixE/pKRNqhF64IdFZGjvYgC6tADJM9TGR/8HVGhzQ9zZIiHcwJKSuwgHAKKdgnF8HMwmCjVSM53WQLaHpeafEuPvFBkqckuPwOFFFIBVxWzkRHRTu5Jkkxwd9OnyTyC1IUJU8mSRnLJse9BRHX2wXFpSz4wPRW/xHgQ+ck982KODoujmnTZ5A3aRqryxss4amxAbrkqWedz+a1z3DGufO4+KJSrrrgDI6OjbFoWEBoeMLJ+Dr17OuPtXEk1syZQCUw81B0MBk2Rvlg4GQI9c7W+nuffdbGdUuuw9vdw5TvLFEJKFd8Ir6OIJPCFZ+oeV027qT2cMvxdbj31eDGLITQ0ReFL8dXZ6HYVbd0WrxhMD1K4TcDQc82sE+JEzs1YdU9QGkuOtn7Ln+5KthcFHzK4wcs8Vo5L39rA90NNUQVlATDNEiPvGARiS6GD6bRn5ufRnWq88Qi55npjiZptBlGSdPKzu0i/oK0+GyycoJeYF/VaDs+7SJvtGmQV27cSeX69fg62vA2F1GmNbzVWRgzC9No22M6RcK+0Mvb73vLDK2Y7x+guqU18NukMiU5uNqQ/oZCQRTDmlUcqtklE0Lrl0eR/+0r+NaZ32bDquVMu/BKfvL9+XznW8eF7b0HznFlgR6ftt+JIZWyNqnQfzf4/f5ewzDmHaqWUsPOKA/W43SC/WbaXfYq37/5AU6adjoxE+bwzPN/Dat1kT97dr/7lpZIc8YmWo4jS0Wd+xmOMRLUccgmKSIy8LdLPZhSGSce3XRPsjJ08wvTLfsUnrK9ohAIoXvJQyltqvSebn69s7LDQx9VUEKtK9vCy7aXkjtBV7+zq8UNBk7nJ9RD4YLLxOA0Fqccgc4F13U5dIhu9LrKRsUZ94yKo0rTk5bvCi0RsvGkHU/RN90YesgrIRUaauh5cw3TJ/Qtprg/JpGSH9xK0dxd/Ncj97Nt+wc8dFUp4eWfgtcE+u9qEg5Ojs1QIDl2RNh7JOaLqCE91leF3+//+6Ha97AzyoOFXedCIDfWh3//iIuvu5V77riNY046jUWPvuVokEeNL+L5e7+tXoe7efbu71HGcW+GG7evSS3nAVP4J0yFlIyxscPH8jfNogXdA06Ldymvray+XVHC6sfEKe6yhAAkgSbQhYl0jQ2nij9dN1lxYm0dLCJq38WteWS9WosngYRFZN8Su02KiWVztePlUxBhJV1iVK5VlstnaRqrl5nbDfOamuCEo4d+nBrK5o2OZdHp41i2p5P697aobfWCEUcUpKg4v8TG5xemq3Fsrm5V3/c27KRyPSSkmw1iE1LMApRFp49T208qLqX+zmuZVFzK7vY4y7j1iSuIo1lwz3Je/829XHzPch5/8HbiBjCnOXnTA2FewL+vl+wEwzBu9/v9dw/V/oaNUT5UXXNXPvEU11z1A0rOPCfIOHBAdoHHsVR5IOOS7VThg63rtL4fXYwegktWvbQaUP3n2vaYiTHx4BLHxFHV5FX9AO3ftWg0O6jHCexdLL5oqFHLYj0OGdHaYNFQ0OVG7RrSeghE9+ztUqByPutoVJQ5XfVN12awVzYKdrcfYLX2Wtdu1rudLDklU4VfAEpzJ7KuIIX694JcZPkt6h0aAwja9nQqo/3EM81UNZnVimKMsws8QAoJKSkhxl03yILajbtInbGGOVMvZYemSa0nRfWilU+8X3LpzQ/y2lM/49tXXs/T//U46Wn9x337Mq567Dicc3MEAMwE/v2MMgy8fFpHXzfRx41tbCvbztW3LhtQexsntbWQsm9cSgGtscNHVnKqwz5cIUlC/fhZLh9LTskMGZNTQ9XEMXHUVZhup3hdYHp39oIUuzi+XavZ/uBJoUskpscskBCHEo4PxJezXFLA46wBostApsW7LLrOOvQy5t0BTrZ5/lhKxgVO4kbiuYrh0otVZAJr22N616W5qXS8vIpbfvh7ihJjmPvnP7M14MmKpy5xwJ5zhgAAIABJREFUenMcwftgdXmDhZYn2BpQpZNVV0tVGaPGF5Fd4CFxjIfK9esBeOCnsykKNKIV7z2iegv1zZ3cc/7PuWLxNqZcv5RaVzabq1tN7rp48SeZMW0xzPWdX3Dx9beRnvYE3/neQp5c/muOmzBejSlcgq6vDvF2XnNfyewjGBoMK6M8GIM8kBvn9T/9LyVTpxF7dP8NPgcKXZLT1E4OFBPEOHiONujKcFkua3GD3chJeEO8ZW9zc0i5r7182e6N6V6toukBEJz85KHUZSH1B1WkL52uQ3+TqE4R1Ce6/IIUCCQvxWAJJF4PwYnJKf48M1BNqPcJFKqbFLGAKZCUeUE+U85ZyPcDhjQhw60mOD10EumtC/QnNBXZznosGOYyJ8MUy2sxvIKWqjK1X0keSwNbKfbJcplVkzs7u5nuScSdm053xRaSpuaqKklJMOr0QUFjh4/SS39AakoKC7+/iOuuWczZc87kGFtv4v4KQfQVkR6D1kX2+zLm/2YwhnJnw8ooDxb2m8buNc88dSorn3qWs5v34kkJlrraGRdgelX2TiB2D1cMkB5H1btWS3GDE+yx2CRNSNxukPVlen1zJ/kFKdSPiQskkLBU0IFpuKtbOpXqmi7rGX4cpteme1Z2L8tOibNfXztn257UtLer0ruWTPckMykZzDbf4TuwOInni3erazbrn20CVZTSlpLC5upW0gqzOWH5z/G3NvC7+nbFaNH1P7orTA91BLAjYaIyuhknlZA4Jo55Z4xTx9xU3hhyD4EppqSX3rTt6VRJRM+oOCYlRyuVOr1jttwLnlFx3LzgRMdroV/XkjPP4cm0VB57chUP/PxXTJ02jeKpU/FH/YOY2FjSEt38x/jxuI3gdbV70vbf20lN7ggAWN//JgPH/2mjLAjnNY/xfINpUyZRseEFvrVoMWB6H/Xvhd+X7tEJhQqCGXhdP9iuZ6y3hQIcGRI6zMRZUFNCP7YY3rY9ZmWZbpDFGMt7EOQbN3b4HHWOZd86VU0ve9ahixkJ7NKR9gfX1C121nDWr5dOnfskDOFIZ4zo12Tlxp2WlYLO6tDPNSMljrr0ccowS/hhfmE2jQmpoMW2TRaMeV6TikvpeO5BYoE8TwkZJwUpcRD8TaqavOYKZtq5A+6Ebkdbdw+f1e4jO/A60ttEUUa2JcwRbiUibcdGjjuJGT/0MGZuA96/vcXja9exr72Dnu79uHzt5H3jOB598B7ivthHT0JqiNG1PzPhpG//3eH3+4dUJ35YGuWDocWF2/6Kq69j4XfnMevbFwHmUjjctGf38tZVNipvqS59HMwKSlGK0Ly94ENvzWR/qLJcPiK9JlvD7kXaWRLjUxOUMalv7rSUW+uQuG1STKQmyoPynPV9ijeqx2hFlEgocrp3rKvMqaRfACJ+r0MMin7ecn2me5Jp7PDZGCJBmlveaGvBiX4d0+JdajKR89cTpPbzEwgDQiD6G3J9BcEEZDR5F99FZMAQ3bzgREuMWrRKwDT8Znihf8Msv1t1aiedYxNJnTGZxOV/pb65k5yaBtyTZ5hc9YhIxVeXEn1/Q7BgRITu9fOsbukkOs5NXeZk0jMnszfQyfyKU4/ljScf4vqbbuHxe37a5/jAeVV4xCAfGgw7o3ywPOVw38vKzibr2Byqtm2g6IzvAOZy1P4g5RekWPjFbp8Zb5MlaktVGVuB8YF+fVKaqxtkeyNR3eCA6U0mafQ5J9qdXTNZIOprOqZrQkdSqQahEqF2YyWfqyKTXqsx1r1ie0shfRu9hNxpgmns8LFy405Tva7Aq4pcBNJz0DyvZEuSzx6Hnu5JVpOJ/ToEJ77ghMasE5R+hf166K2pdGaI0A6lylAmhnWY11VYEQLx2p3CYQJvczOVgf/XBhKPFxWUcP7p5v2XOmOyRTxKL8w5NjAJRhaXck9ZK5s2VLLo9HGU5uorIFNn2x7KOWrECEqvuZFrzz+dAwd8RBPKtNAhJeiHEm5XZNgchBF9+HTNPtQYdkZ5qLHx1T/R2trC6TNOJd7lgww3iWPisEvujE9NwN9ap7zBfdteY/zYyy3beBt2UtV0gvJOnZqE2o20PPy6WFFfHazFsxQDpHc/0Y2SbmTK6ttVmEU+B2t4RcYh31MdqnF+EJ3Uxuwc13CxfP3BU1Qyh6SVHbqnJsJCejhobn5ayDW3eHQxsYCL6Z5k8zzzrROkDns4qbolyEHeVN4IhWkqRq//HkJ3SxzjYXxqQoihdoK32Wyq4G1IZOWYOOZcXaQar45Iz2W/7dxXl5v34JJTTGN91R8+VIyVZXs6ybzmFCYlm79H0shcGrXONgJh5+SMHcdfq5spSTlK9e5zgpO85xFYYRjGGcBm4Bjgx0AL8Gu/3+8sxh0Gw84oOzVHlaVVX5l+e7ZZXq/979+RmppGU8cXGAmxJMWYsdjta6zfr2rywuigRzjynIXMd6WzVmucOu+Mcco7bezwqW4gAj2hJcvt3e0HyBsdFCuSBw6CBlIPhej6w06qcPI9PQwgBlkoYoBK+NmNkcRzk2Ii6SQ8g0XPzqvrL2pygXCG/BpuIG90rqX0WxpxzjtjHDBO9eYLtq+PNb08GxVNJhC9VFy6nsh2TpMhBL09aWgr1z/THc2csYmWbe0dY6Z7ktk0JigEVdXkVSEX8ebnavQ9FcM+AzYFaIv2oiShyEkIzNfRRuX69TxSmMZtCxcGuqm8RnzOROWlvrKrjbWvmr+nVHWKMJKEYxY9+pYSR5LWVcI2gSD/urKimdysPNZv3c7URd8JuV56bPnfXOuiXxiGcT9wHnAiICLsecA3gEsGs69hZ5T7w2ALOn61fCXPPvUk8759LmddcCE/uvYapnuSWdtPkqZ9ZC5JmOJEYA016DQzCU3s+LQrrNSnaC8InEIR4bC4OEct83VI9VxZfegkrbeO0mPMlpBFALresJ0iBQGusoPSnb5dpLcpqF9s87ztJeEW4SgXlrZa4bp/CPSqRUDFr/cGqhdlv6IapzebjevtUnHz3pyJStpzc3WrYoL0J/4kqws9LyCTdFVKHFsJ8pZd8Yl4G3ay3aF6dFN5I/Mvm0zhVdNor2nAXfsucTkTWfBirfKIE1JSVNstX0cbrvjEkP0InMYs+0g/7kTee/lxcDDKdjhV9cGRZF8AFwMnYKrGzQAyMKlytYPd0bAxyron7Ci4HePsCTtBZw7ExsZy1TU/5NsXXMC9d93JpRdfwt0/+zXzzhjHWs5VRH3TCzLjgrWubMoC1X/iYcny0h56sKuhCSTGZy/9nV+YbhG911s66ZQxCVGYxjzZEjM0DZMv+HeAByuly7pBdiq80A2bnE/e6FhlmO1hCve+GvOzfcFKPym7ToqJtAjYCCy/JcEknt4c1omlIYY53KooKxCC0scuE6YY3SRPiVpxTPckMyWqiX3PrQJM8f6o5HQIFGtIMjVvdCYrzj+eHcU5agzi2eedf3zAqG9TzVdjs7PJKSiB+GxTB6PJa1bzNZjx5XAxZkFZfTulC26E5x7ESE7nvrJW1QXHFZ+ovN1lF+SzufAGgBBGze72A8p7rwy8JyulxDFm49pJx43mL4/eStu+dhJHmtdNft84Gz85HA6nkIZhGLOAR4E04BXgcr/f3zHYbQ4CUUAXMBso9/v9XsMwUghKew4Yw8YoC/rqSv1V9jVmTAqPPLqch/7zZyxeOI/rlj1iZtcLUqhv7qQow403piSEtnbVHz5kbn6aahuk83/tRQ46hMuqV5vpWhFJMZEhBl0XJJL96uMRr0ySc/K3dLuWh9bJ49QLLMDKRNAR8nDaeK2R3iZL0k8vj4bQ38lOpQu3mkiKibSEHWRfOitADHWWyxeg4AXRGRELnhJzAuntUmyVvNGxdL+xxdLayt/aQJInV4kKeUbFKadgUjLkjU5UxxRR/y8aaogsKCEyZyLG1Fj2A/X7eywqdoOBXIeR5yxka3cqTzzzP+ozX0cbNa+/xBPNRWwq8ADBpOKcQIfvrd3Ba6qHVSTuLpNx5FFHkX/iRLZu/4CzT5tqmXDDGeOhEAU7FDAM4xjgd8BlwBvA48A9wHWD2eYg8V/AdmA0cJ1hGMcDTwIvD3ZHw8YoywM3EGGUvm4YebictomIiOCmG37K/8s6lmVLruDhFU9y7+wJAI4P17Ln3qfm9Zd4EVj6wA3cUmQaxSwXvN2KxTu1J5T0ThwiW6nHjsVbtcdHxajadR4EehhFGbxAggusjAbPqDime5JDjLSwRoSqJmEAfTXiVEYd6hVbt9HbDdlbGOXZ5EzjeruI3NekONESvpDzTot3WYy9QO4Hu2iRVAJmulOZBGpFEtfbRYQmtCSl4+59NZTmplKUYf7+UgCUFh+stpSVwRcBydJaVzary4Jl1yKxKoyN+uZOavrxkBPSxzGzMM1cAfV2sbU7lZt/X+koktVSVabe344Zo65qOoHnzsshzeeyJHAXax4+BHMS1S2djCv8Fm++8x5nnzbVsn/7b6RPnodpufXZwPt+v/8lAMMwHsD0hK8b5DaDht/vX2oYxquBv98wDCMf0+CvGuy+ho1R7gv9lYyGCv44fyb4Xul36D3QwQvP/hcrf3a/owEvynBbhGXuv+khNpXO5y9X5av3nEIDuieo6zLo79uLKQT2IohwCS2BheIUSHDp0p8SPw5X3RcO0lhU348d0m5KvHbdo82yedd2DixAnCsouwldFoOsX4O43i52KA86YKVjgtKScS5CvOZPfGaMOcsVaINEqPC8JCqTArFhCJ3wnMTgxbsWVFY0U//eFtr2lIRXmdPgbdjJ2ldTLCXiGSlx1I0vckwU6u+1VJWxvmEnC5jHvbMnqIRyVZNXORdimPUk8bHjjuP3f3oBcG7Q2hcOM8OcD5Rrrz8ExhiGEef3qzrzgWxzUPD7/W9of1cSjBoNCsPOKDuVNdsxmGRfuO3PO/87rFz+GE3Ne+g+xiqDmemOZuzoeBp+dyVRhcGHYvua1dxXeAPzC9OVfm99cyeZxdEWilVSTKRK0OkVbE7nZW8LZV/e68kqHeEeFvm+vh8JDcgDa9+nvfR6c3Ura1/daSrSactm+/F0Jb2gpxqNU/cTWYkEG5DGWoy/IFiOjEqoidEGLF69JO+OBUifrLYRnY9jG7bR3VBDVyAGLLALxYtyn35t3QSZOCLWlBQTyZyxiRZt6Pr3tuDraFP/9wfZth6zIKky0DTWvF9MCpxnVBzLnnsfgJaq0O9v3fABt2J6xyLMVKat1vTf1jMqjo83f8xx3zg+ZCx9NVl16urzNSDdMIx3tdeP+/3+x7XXCZgtmgDw+/0+wzC+DLzfOYht/qUYdkZZYDfIylhrXlJ/N0tfs3xcXDxz5p7NylX/zWVLblDvZ7qjmZobTMR1lz9FVOFl6nVVk5dGT7LitXqbm9lcmEbe6MwQOpjEgO3hBzFSIpie6Y62eMV9ibGbsHqMlutDkPamixDpfex0Kp6MTxfMgaCXL7FonUEitDi9Q7SdnaJXpklfPBmH7M8OWXmYYvIJZg/AwHk5XZPOiFj2pk8GgkJRFtZFxmTTYNfVqe901dXhDrRT0qmW9vutXdPFjgy8ln6IWzd8QHaBh5mFaWxNH2cKEQX+7w+u+ESlpSHIdEczJaqJKceZxrHnzTWU3lnKfWWtKhktBn/U+CKmzDpBeca6RxyOvfJRxXtccMo3Qt7X2TN6yOlQGeFY/4Gwz6PPvx+gwe/3n97HLvYBqvOkYRhRmDbus0Fu8y/FsDPKA1GKsxvb/pgY4XDFlT9g/nfncXR6DllFp4XdTjfM9c2dqkW9iJpXNZ2gHm47/9XuBdv5yGWauA5oGf/RJutDvEmnUAm4VAihsaPLwmxwqrDTk4D2MIl4qHmjE0O6akgRjBh+CT0EPdng8l/Ofe/+HgtNzS7WVFZ/QElm2r1lMA30aqwTiG749TJ1ne6nrwYAsnInEqv1tZNR9CSkWiaycELvYLIUJOa9dcMHgUKQncwsvCKgoxwUQOoP+bNns+j0cSpx3NjhM4WZvGZIpePlVSZNrq6O289ZyC1Fp/CJ71RLLuLYhm30unx0RsRy7+wJlNW3W6o59XslN/loqneU882rL7SMwy5MZOekg5VBc5igCjNmLJgA7PL7TYs+iG3+pRg2RllPLPQ3Uw/VTTJ6zBhWPP4Ely5cwJL0TMYdX9Cn0tuo8UXq77n5ZmKHAg9z89OClWVa0q2xw+cYTpAqMR3yuZ1xANblqP49MWZ6FxKphLNraeji+QL5TDd8e/f3hBRaWD1vkK4mAy04sMSbXRqVLwBJRtpLhTeVN7KJoBiUvvIoynAr719CREKX088x0ttkSUeKcZZqOHusu78ipewCD96GnWScVKKkNtfu6QzxkoVXrIc09PsHgkUx0in9i4YaYrOzGXnOQtpH5tKOtchFCoLK/MdTFBCzkklJWBcSdiJw3dqazbh4ZlqKOm8n6L/lYDXNv0a8BDxsGEYxZqx4GfD8QWxzUDAMIx4YA0To7/v9/oHNyAEMG6MMfRvbQ3Wj5HrG8qMf/4T1q1dy/lPPht3u5NL5lteludFkXmAm/eyGXC8NBmuYQIyRbmB0D/cTH0o5LhzEcIlHZA8FCPVOxqV38bBzhPfu71GUOghS5cST0/ep9wh04iSbCBpvp8lVJl77d2SCEMOzubrVUhQxPjXBsjyXwh1dqhR8ytCpe8lr7cytJ+9kErWHgOyG+RNfcMWTkRIHs2crZoNnVJxj2T6YTAunTiTVLZ3s1Yp4Ir1NSjY0sriUWp9LhW3s3WRkVSPhJn2VI9Cv2z8+3cnJJ5+MYThLAusFIuGcocOFHuf3+/cahnExJuNhFPB74H7DMBYCt/v9fk+4bb7qsQ3D+AlwH9COVV/Zj0mTGzCGjVG2VHoNojhkKDD3rLNZ/uiv+HBHJcfn5Ttus+2m6Ux+YLO64SO9TUxKtjELbB05dNF7eV8SXmJYdVEhe1m0PY7q5DGPT00IK2wkCTc9kWXXgbaHPOSYerGHHE8mgUZPsqKj6doT9t/MKWm7d38POzpgd7vpQdorDE2DbZZgg3P1o4xXn4yE0ra5ere6rlIQg6bpkKAZZfntFIuEUP1nuQa6WpzEccWTt1fUjRpfpMTuZxamMd1zirrOU6Ka6K54Dd4JThbdFVtUMrLnzTXkBOh3+u+ls1P0VcP8wnQIVE3uzXCH6KOUPf0SU75xLP7WhpAu2PZknlNI8HDjLPv9/j8Cf7S9vQqNmhZmm6+KnwIFfr+/qt8t+8GwMcp2DDRmfLD71fc5IiqKSy69nCceX8kTj/wnX5Zv4ajCM0K+u+2m6UQVXsbi97awcvZs3pjVAJ6SED0L3ZiKt6oXRzhR3ZySNOJZ60ZC4tRSRVbV5FVxafu+dIML4ZvByvjsYvP9lT0DyqiZ6LvlllMjVJk8yurbKcpwE+cKVhDq1Y/2sThNYDpHPFgNaU3oSdduGY+uRmffp1P3bMCxyaqEJsQY6yGXvNGxTPa+y+5HV3H1L9+iKDGGkoWFwDbcuenEZmfjnjxD7Us6kehUPclj6AYZzN8toXoLRnI6cQmpKgGYFBPJ3rqPefi1jfzojKUhKn9OcAofHk4G+V+MPcCQxKWHzCgbhvEm8FO/318WeO1YymgYRgSwGpgFLPP7/T8bzHGcBIkOxWxt3+e3L5jHf/1mOTUVZYyNP4raH11Ezi+cQ1G+jja2r1lNVkURz9/bpQRkBKYIj+lJSVJH4PSQiyfmJDEJqPCCHuqQDsoCeyNW8ewyUuJCWkWFM9JSkAFWA2o/hp1pYRfOt5dQS6m1/bzk/1vXf0RlRTP5BSncO3uCheesf0fGsvzNWsu1stMJJeknLBE3WMrExTAfSwM5yengg/aYXAsLxX6d5JpvKm8MS38L6lOkWCo5d3zaxaSciWQuhO9Xt1Bw949V+63uii1EFpfi1Vdc6ZPBpiaY6Y5Wv6NM1mKQIcgHb+wwtT/279/PVUt+zLUXnkV22hjLOPvStNDbk9nlU//NcQvwhmEYLwCWrPtgO11H9L9J3zAMY5ZhGCuBKdp7Usr4U4LCHPcEPj4N+Aj4f8CphmEMSYO8wVT19YVwhSgxMTFctOBilv/3OnpzJuLO7d+zAKvRcioesHvFafEuZWTtBlqWnuFKkQcKJ66zPR6r/y+w9wuUsUKwW7QYGiejbT+OTKh2iJFzQmOHySpw+n116mBVk9dxH3Jdk2IiFc1LDJbefQPMxBqYWiePvLVb7XtzdavZLbu8gdXlDcqDdmbAOGN8aoLj+E5Y/nOrdnVxqfq7rL7dsbJ0c3WrI0MFTHEluxfs9/u5+47bGJ+dxsVzpw94zEfQJ24GmoF4zFY1+r9BYSg85SmB/ejWpa9SRgOT2nlU4O9BCXY4aSf0JDjrvB5sW/RwMenTv/1dvnfWaZT9o4VTtOWkju7yp3jvrNPIW/07Cu58i8XX/ZJ1T99kCqQHst46zauqyauWzrrXof/dX5jArokswkbh6GKNHT4zG59qTQgKdG9Qp83ZleXE47QbFzEc+mf65CMhib1aQUZSjGUXgXJmU3EtTxMCWv5mLYuLc5iUjPq+Hr7RKxZ17rX9OuaNjsXf2sAnruzAeMzfe0pUE/teXkVsdjb7p17KC7vaWFf5Ucg131Rudp1JSB+n9CdEHjPjpNDqPT2GrK920uJdZLyzih6gc+qlvLKrjUx3NJOA51uOofrvrYFS955g3DqwupICG70NlR6+2PGp6RUnjczFva8GN/CtxDHcce8dNNZ+zG/vvNYxwaevEkPEpwh0tdYU/IYqfxPxeWtY9kdkx74hOcYhRB4w2u/3f+WL8ZWNst/vvw3AMIyZ2tthSxmBjcClwD+Au/1+/+df5fgDUa86mPizUyIjPj6BOefNY82qpyi640a+LH/VMbZ80h//zCIjG677lXpvxfnHc9UfPlTNOHe3mw089SSVnZUhnqpINNr7z/UHCYtIwisrwF3Vq9Oc4tdiNHS+tBwzXDm4jNep/ZUTnU2HXsSyurwBz6g45oxNJHKfuXxPSG9gUs5EIFolLiO9dZS1HNPn+O2iSvpk495XAwEPUiactHgX/oYGYrOz6aqrI77YvG52bi8E+/wlpJihCEnyiXqbToHTwxYytpseXk9CSgoVd55C/WvbzH1qXvGpKyqpXL8eX0cbm0rnk5ESp1TiYD4wzvIb6Ofu1O8R4ED5G9zx0jZ27m5k1V3XcXSG2ellsFrJdhW5IwDgGUz5zhVfdUeHKtEXtpQxUF9eGvabARiGcSVwZbjPw4nd97XtwcSf9eRGUkwkV15xGefMOYOWq69gVM5EuttbiXInh3xvpb+O0mfewds8jrR4F+59NSwuzlEVe5nuaIvXpNOsdJU5ndUgfdx0Y2jyeq0ZePFgdQbF7vYDJAWMtC4Kr6vHyT5k37vbDyhjs45gTNuJNuckmG+P5epxbzmGHiIpq29XyclMdzR5o3NxFwT3Zyq0Zaq2XNV/b7WwCSTuDoRMArpUZ97oWGr3ZwNBXnSOrwl80JMzkajkdJOvXPsuc8aWsEMzfLpxvuKSEsWhXtkczBuI3KvA19EWEA+ClYHVkrxeU3Mi35kxma66OiJq3yXTPZGbf1/J9jWr1fe3r1lNpaaXvH3NajJSFqnzljZa+m8iiVl53dvbyy2r1/OPPXtZtewm4jM9lgo9uzSu7sgcEbgfEE4FFhuGcS/wKVoEwO/3HzeYHfVrlA3DuBlwClQ/6/f7L3d4H4aglDFQ0/544PvqBPssjR4AVe5gvifbAJCczFlnn8Pyx5/gjptv7PM7ay75ppp9al3Z5I2MtCRIRIFNIMvNvqAvVefmp0GAhytw6lIdjiUgcPJ+pSxZvHO9ek/2K9ctXN9A3eO2H0eOoXvt8rleup0Wn60MpyTlgkJFWLaX66IbZtmnFE/sbj/A5upW1bILTJ3lrQkTAZgUeE/oYXG9XWZFHcksf7PWsb3T2ld3qoYILVWQ69AgQbxluZ51AVpcaW40HW/V4Z48A6+nhM1v7VbbS7LQFZ/I7MvmKU85d9q5lnMUY+zeV0N7TFBtT4xsT08PP73vF9R92sZzj/+ao2NtsSJCq/PkOZNqvnBUuSNQWIwZ3RlDkKc86PAsDMAo+/3+ZZhVL4PBIS1l7Kuyb7CesC5KJPsOd0zZ7vuLFnPO7DO48JLvc1ycDxw8ZUF9cyffmLUEgCefvFsZiXBUKycDaufhijSkNOzUoceP9UovsHrj9uWtziyQ7+eNjlXCSXYEec6hMe9wFYZOsJeei9enXw8CTAK7QdbjxxLigeD10juB9OSm8rY7mpt/X6l4w/ML04mofRevp4RFt28C4H+XnkqWlqMIXrOuEKpb/Zg4KiuaQwxwzesvWShw+QUpId3Gn7/32xz/pwfpefOfxC+4kX/4XDRqvGo9Lj1l1gksLs5hcfFDITQ/vTBJLw0Xfni738VtN9/E3uZPWXX/UqJTckKIieGS5PK/G2vuxik0cgScjFk8Ys/2+gk4lwPFoQpfHLJSRr0QQ8dAS7DtsHveduPsdMMmJcVy0fwFPPbrR1j24MO4/7mD7ootxMy9OmT/+jL08stvZ+kDN1gMXzgvUw9FVLd0KkPTV4bfbpAthk3bRoeEECSJJJ6Xbijtmst6+CKut0tVzelj14+ln4d9HK/salPCSzJup3GacfFUsM0Psl/Rp9aFfCQ+DcdQ5HKRNzqSRYEuLNJfLytnInG9XYo7nBQTaVLjAiXWso9Md7TZjebV4LH7ag8lMeWWKqh53fR2l//yOjWRTolqYh/wzoRSbl5hhivOW7KIuflpSnlPuoPMzU9jd/sB5oxNZEpUkyocyfHV0dMbnEDMySpY6k5MLA/c8EM+a9rDU0t/QEy0S3ofOGpaA2p1cJhJcg4HHN7FI4eqlFGfoZ00CAbqOTsR4O03YH835MWXXsacM07j448oyT6iAAAgAElEQVSrOGlsptJMsMNJRU5vrurEQQ6nHQymgVz+Zt9tv/QEoaCvUILEjtv2dCpxIbsRdmJwJMXEEult4tjWBpI8JUqeU4cT+wKCyUGZDGSisrNIGjt8FtEmO09bjiceaNse00DLakK0McQQzxmbyN6AHCqYXUTc+2pYdkG+iv13V2whrjiVHR3BSVC+71kQp+LTc/PTBtStGsy48sqNO5lZaHYBqXWl03jmjVx06/8oA77+qbXc+8Idav9ikPW8wpyxueTsM6l6ul72Jz5XyLWv+9vfePOd93nt0TuJDST1ImrfdSwUkYIZ6WitNyTQj3UEYXH4FY/4/f5s2+shL2XU2QkD0bqw6yX3ZWj7KiN1wtFxcXz/ykX86uc/49kVj/Q5Dt0w3zt7Areu/0hRl9ZVNlJZ0RwSohDDrYcOxCDpWsyCvlgIdg/VHrfWY8dgFl8I20P4tPp3dK7spORUInFWenPqsyeQ31IvOJE4NoT2JpTzCCfUpAvLi4HWX9c3d1ooY/qk1JNgdiOJ9NbRXbGF/VMvVYwVvZWSTKASxhifmsC8M8Zx/+vOAkN2bF+zmroKM5Zc1eRl/VNrLdsnpI9j2pLfKQYHmCEqMfzmeY2jKCNbtQuTZK9+7QC+kRzN/Cvv4CdzJnHU3nr80aG5CvGWRejfyVg70U31ENhhLE70dePwKR75uiAZYSdtW6ftwnm+Byvj6WScL7jwQmqqq3nn3fcxktPZv+6xsN/vLn+KpsuTALMMd+2rO5Xxg9Bih/64yWJYnBJ4Ukxgp4PZizcg6FXPzU+ztC8Cq3GzF5ToxlH32PJGx1r6DEqiTC+I0SsYPaPilOGL6w0mOsONV1DV5GVdZaOatKRDhz65CCTEIOdjlyztjIi1VM+JHKpUxul86/rmThXrFaZI7rRzgVCDPGp8UYjyW0tVGTWvv6S6UPcHGbMcs7qlk8YOH3v397C5upXV5Q283YplzACb1/wXeNuYe/JxjEjPVYUwdkgHFTHITmL2e/f3qH8QfBaOGGQLDqvika8FAxVAcQpV6O/bG3l+FURFubj62iXc/4tfs+r5/ya+ODUsdxmgbsMHdJ7po/69LSSkj6MSyC8wvSIxTLIkl2X9sguCAkh2HWRASVSK0dA7MIvhluVvZUUziWPiWKdpIQt9zl5Fpxtkva/f/MJ0tW3e6Fh63nhahW56cyaGnHPw9+jplwUCmlwlwVJmEULSS5n1cYq2sxOfGIKC/HJO9pi4ILK4NBAGaHesnizKcJN5QT6bAyGIJ57ZgrdhZ9iS6nCfCZ588m4WX/dLpRZnH6esVDaVN6r48nRPMpOS4e1Wn+Vcn3hmCy1VZbjiE0kvKCJiyyqeuvFKXCeYffdGaMfVjTA4e8gQ/O1CSuJtlZ1DZZz9+1ocW2yZnw28WvJfhMOneOTrxleh48hNNNBWUQPZbt7sGTz95BO89ueNnDuzuM9tP6vdpx4OET0Xwyyep/yvG8VJyUGPxR62gKAhc+rWocdcTW/LLHbQqXWSaNNDFE4eJ1g7bkNQycwUsw+N+8v49ESg07I32Jg02DVaztmJJmhP6jmhrqJadQDRDdju9gPkjY7Fva9GJc1EO9leWm7vCjMpGSYlJ7Ompn8xpr4McuX69VQXprH8l2a/Tj1mXp1qdjpXeYbAJCAGuTMiVqnoQWjy11f5IuMn5HNysWmQexLMEBMQ1isO17lax5HEX594hsO8eGTIEY62YzeefVHl7IyNgRyzP8McGRnJ0ltvY+kNP2H6t15gZB/bzvzbdrrbWxXdSQyzlF9L6KBtTycrrzlFPYD0duHeV8NeV7YSKRcho7jeLiSlbsZ0kxUjQS8+advTibdhJwkpKZbkkcRbwTQI4oXajbIuWqT300tLn2waMocJQSCFKgJdNlT38GUMogYn28p3pbhEN7JiuHTZSggotTXspA4z3ixl5XpxSlp8Nreu/8iyH4Gdly2KdVna/JCQkhK2xdNAQhNPPLNFUeZEva2xw8fKjTupq6imKuAZyzXKGx1LJ8EKTZ2RM2XWCTDrBHo/+gv12/7K6tseBIKGV7V2Chxbp7dJF3J7T75wCfHDTa7zMMHXVzxyuCFcSx4I5Rwf7P4H8/3OiFhO/ua3KC6Zyr0/f5SH7ljaZwgjyp2sqFVilLevWc32wOcnl84ncUycegBTSq4BoOupeeQko/QTSnOjIZB1l2W4e18NeaNzlVCQ7vVJsii/IIXvGh/yj8LJ3NrkVctloX3pxlgPcazcuFMtp+1NVcMVhwhe2RU0UPo2EoN28vDld9U1pwUymUhFZH9caIHImUKwA4v+mZ11YocI5UOg9N1WuSfQCz/Cfa6HLManJig+NfHZlm2Xv1mrDDag2k7JmHW65NjPa/jPPz3Hmh9diLvbixnetCJcn73OiFjHsmndADs9F0fiygqLh2pHw8oo6zfSUMezwh1noLj+pzdy7twz+cucuZw6cSL71z3myFsGuGvWeACeaG4O8bQq16/n+luuAIIGGWDqhii+//A8Nj17DVHHjaIHs+OFLPvzRpsdlsWjBvO9KVFm4YTJNT6ROWMTaX/2Do6dDCvON7sj6/3yJp2XY7kOOz7tUmW/lfGJ1M+eDYVpFu6yTt+zhxr0/nBgFWCXCVBvIqs3Tg2WkUMnsapN1tpXd+JtbmYTgM1rBjNUk1+QQn7BPMfrX9/caaqzaN/JdEcHQhPRIaEJ8d5zfHWmjOfIXKqavGENb0L6uLDx5Nxp55JfkKJyCMLhjvSaQkg55yzkmctO5tb1cYqdUd88nz9eXeQYXvGMiuNzbzt8UslD/3kfK644i8xRxwBmqEJ/OvpiUkDo8yWTvZMxPpiV5/9l+P3+N4ZqX8PGKDsZyoHwlCGUGmf/bCiWYvHx8dxy+53cedst5L28juTi0rC6GGAaZs+oOBZfF3x4u8ufsmwjr6MKL2PZBfn842Fw3/Yeo8aPYPdDk8gBspID3k1vl4oX6iWz/tYGIlobyPOUsLm6lR3uaCZPnmGKnvd2EVH7LkLG02PD+26/gsyFC8nzlPDMZSdzCVg8ZV3lTgyM0Nh0IaRMd7QyfKIrLYUbZgzZpWLie/f3qHBCkIpmFciX9koQpI3pqAp4/xAa2lC96lI7ld6wLgj/diuKKWJXwsvx1QFmuXzZrr5DE311ra55/SVqXocXA6+XPnADme5o/K0N1G34gGwga8GNzM0PdsIGc7UxZ2wijR1YmqB6RsWx6TfL+KJzH/ddOIMTczNU4lVP4A2UZ6zrvIRbMR4JXRxaDBtKnB06V9L+z45w+rvy2WDCFeEodwDTZ8xk/ITjWPHYr+mMiOUTn4tdn3aE3deCEzPo2PKfnLdkUYhB1tFd/hS/80zk2HfeBMyHvichlfaRuUTUvqtawNvH2BkRi9dTgpGcziu72hQNT3QdPvG56M2ZyBcNNRjJ6dS6snllVxuv7GojdcZkRaPKcvl4Y1Y3K84/Xhkxic3WNwcLTpx4xBAMG3ibmy20tM3VrYreBaZXvXXDB1RWNCu6W1l9e8CA+xQLIyMlziKDKcwLp+OK4NK6ykYVL5dGsnv39ygK3/I3a9lc3convmCzWDHI0gS21pVtKWQRKtxXwf03maXTvTkTOcYzivIVrwdbU6WkWCh19nu7qslL5a5P+FtFOU9dMpOZBeP6NchOVFH7PS2hMCfYKXJHSq6HHsPGUw6HwYQvwpViH+zMrxelyD6mTZ/B7//7d4Ma25pLvtnvNiv9dYDVm07RKgWffPJuSnNTTSMt4ws8jO0jc6GlTcUv4QvV7DNpbCIJ6bnUBnSFJeYbNcoMbUiJ0hcNNeAx3xPvV/dKRZ4TCFlm67xkMZ5ShDH9mmSyXD4iqt+lNHci1ZeUWM67uqVTaXfoBSdy/E3ljSy7IN8SZxXonVn0EMre/T3KuC45JZOkmEjldcox9KRmY4D+J5rPEi6Zd8Y4nmgu6tMz7g+5085lzthE1uxqgwUPUbTUrWLs+QUp1AcmHyeMT02gYcuLnDFzOrGuKMD8ncJVlzrB7pQcqeD712PYGOWBaFLYES7u3CdDI8y+9OM4aWUIXvvzBmadOVt9ZiZv8sLu76tArxS8/PLbuRw4b8ki7p2drVoB9eZMVNchKBX6GY0dPtU1OQGrUH5avIsel/WhjCwutSTs9KQZBMuQnehrcb1dlOZGU5QxQcWIb9aKMDqee5DY7GxoqOG29Fy2JUxU3GgwG7FOiWqi+50tZBWXqiYBwlQBs3u4eLlg7ZrS2OGzeNK6tysGUFYACdVbiMuZSGOHtdwdzFBLdUunSpqOT01gyqwTWN9H/NjeGmrpAzcwvzCdE79zFwArrzmFHZ92sXKjGdqp1uRcJdQiHr+o6una2jWV73HDhacDX6gikS8aahhB0Fvuj+4m97TdIPdX1TrU+LKlni8anE3SF96BJXP/L2DYGOVw3m1fKm9OnmpftJ6hoPyce975/PqRX/Ld781X7/X8cweR/+/QG2aAFx9ZyYuPmAbhf5eeShZmCa5UpeWNjqX7jZdJ++ZC5Rl+0VBDnqfEUlAhSmyTks1rIQZZN1J6CbJA5zHL350RpqxkDtD9jtmZ+S9X3cjbrfkc/6cHqdvwAcd4GnDnptP+2jby7i5hc3XQqxb946xicyUwKWeiahLgxFV2avJqLzmXWLPoiMi5TMqdaOEB66wMKVxRdMYzxgWYEfOob+60iE8JJOknf8v4JJk7JaqJ9pG5lni9rv0hqwTB6vIGxdY5uXQ+sSNG4Peb7CsJNwl3PBycaKROHT+cuvzEJaRaOsYcwdBj2BjlcOjPc5Vtwn1nqBJ9sp+pp05nxWOPsnnTnzl3ZrHyOmo/7WDs6FCK0lDAbpjBLMuVpXdavOkpS8HEvro6kqYGOcIj0nOJCnSIjqg2pSzFKKXFu2ns6ApLO+uLRqZ7ze2B4oxj0xugro76O6/l+Nx02msayJ51gukpO2C6J1kLh7jICjQUnTM2l+qWIPPC/A3N83RiKej6GXrhjMS4xSM1mRfhu2BDkMoIpoG9d/YEczK7Kp+4aTeoz5x6Msr4JK7tr95CXEKqpaJTpwHa7099ddK2pxO3y8X+Az666v5JbHa2JXShuMmBnIOTtyzPwUDCFbKNvUPOEQwthq1RttwUDr3CBrJkk/+dhIgEgw2bGIbB4mt+yC9+9jDTJuYzcqTp6ZTVt3PJU9vZdtOhaVRpN8xXXFLCsufeB8zYp7k8d0GAXSGc5rjeLnpzJppLfx8keawxXXtTUDtP2ckgW4pLAjKg0pF63hnjuCk7G3eAATISa5XZiPRcvAQ9WxE5SooJxu53dKeye1ebKfdZmE5jh48dnwYFkXTes86hznRHB/vcOWDlxp1sX7NaicpLp5i0eDP+LrHsjBSTarfklEylOeEZFcd3jQ9p3vIoeVeuNos5Aqhv9qi/7dWYRnJ64B4Kyqzqsq2Z7mimRAVV4ewJzRGuaPYfOGAxyJLIFYjokNMz0VcYLxzsdLgjlX5Di2HDvhjID6+zLPrKOA8GujEe6H5Kpp3KlOKpnH3hfN7f9U/LZ5Mf2HzQY+kPkgQ8uXS+heplh3vyDCWI/onPZYnF9ge7URHGRXVLpxJCktfL36xldXmoloHT8lo6SXs9JZbY9Y5PTSZAuGs/mERvf11dwkHCPxD0risrmrnqDx9y8+8r2VTeyMqNO/md/3jAFLCXbST+W1dRbZZWO6w47F7nuspG1r66k5Ubd7K5upXnW46hfWSuxYC64hNJHBPHCFc0B3zddNXVHdS5hWMr9Ye+WEiHIwzDmGUYxi7DMD43DGOtYRiOy1bDMN4wDOOA9i88LeoQYdh4yiGeah/EdTsjYqBx4oNVkLPDMAx+cuNNTDjuOK64ZAG33HEXc844k0x3PsvfrKX0mXcGxLg4GHSXP8UdG6rIGx1LxZ2n2D41WQ5GcrpiX4iRmF+YrpKDRnI6RRnZgEmHS4sPxjWlUlAv4dY9T0lAibxlZUoK0z3J3Dt7AmX5acwZm4ix7wu6K7aoxJR4eFKsAsHY77rKRijOYYq3SYVA8kbHquagOz41VegivU309JoTsS4hqv+9d3+PpYs3BItZLrr1f0hISVGJOJ3r3YnZ8qqqycvWDR8otkXN61g6jKyrbFTXpK6impaqMurjE6kkWHb98/ueYP4Ld6j72N/aQJIn12Kst274AG/DTrzp49iEqQm9rjIoIiVUvIyUONp3GXQRRWx2trqWUjTitFJ08pbthnmwjKbDHYZhHAP8DrgMeAOzE8g9wHUOm+cAbr/f73P47GvBsPGUwbx5xBsON0s7iQ6FC0no6O/mOhiPYM5ZZ/ObJ5/mF//5EEtv+AmpEV2q3dGh9JjvmjWevCvNpNOO+d+l5801RNS+S0TtuyHbCh9XYCSn429tIMvl07pfm8t/KbjoD/XNpliQlBFLAlG0OtpH5hJZXKpU5ZxkJWVc0r0arL+Be18NSTGRJlPkzTV0V2xR94YTd13izDc9vJ77b3pI7eeWIrNpgLfBrBKcX5hOlsunvvOJz6VCFIBF69gVn0hCSorlPRmzcIwzTrKGg3wdbXxj1hLuKwtyouN6u9T5ekbFkV3gUWXYIkeqT3xmtaJ5zKOiojlwwMeI9NwQKpyELOxJvEhvE3G9XeqayD8IrfDT/zklA4cJzgbe9/v9L/n9/s+AB4AL7RsZhhENfPmvNMgwjDxlMbZS/nkwGhVOfzu9HkpMOO4b/M/L61i54jHOP3sOV11zLXedfQGXP/s+kx/YfMhizC1VZcRNK6N5y8t0PXsH7vRcegNJMrAuyZ0MrXhUTstbvVuKHXbjsbg4R5UnS8FKY0dXsNlnQYmpY+xtUvFuXYCHsYmWQgZJfnVXbMFft4pE4O8bPqDg7h+zHxx1NATL36xVXu7aV3ey7e6ZrNnVprSNvQ07VUeVxo7QLipyvvkFiyyvdfH8tHgXc8YmMt2TbOkHaNfDuP+mh/A8eTdZuROJqDU52jKhFF12MnAyjR0+CzVQjqNLe6YkJrD/gPlaQkLh5C+d4KRbIqtK/bdPiolUsWmBk2jRYYp8oFx7/SEwxjCMOL/fr8eTsoEowzDeD/z9BnCV3+8fWHuZIcKwMcqCcLX6ThiKG8bO7Bjo/vRtY48+mut/cgNnn3Me9951By/+4QUuv3YpG1pjuWNDldLCOBRIKbmGk0vnsywhH1ohb3Qu7n01Zpw2YCQfeWt3kA3g8ECLcZDEkxR+hEuYiazm3Pw007DuC+ouNHZYjaYcT19S692ZJdYskN8jqqCEyGKzV3jB5GCLI6Hi6RD+9dz8NFXePO+MccT1dqlkaO60cxW9LpxcqIj/yMpCDJnE7yVU8squNqX0ll3gAc1bliarEoJ4uxUmBSbLva5sVpc3KG3kUeOLlPQoBJOsepeVptbPGJdpDY/qiT67iL1ca2Gr6PQ7vbRaknlOMqvhRI2+BqQbhqEv9x4PdL3vDwnAJ/LC7/f7DMP4MvC+fhMfA/wdWALUA78CngNO+6oDHwyGjVG2d2Huq82QjoEUhAwGB2uYATxjx/L0c79l3f++zM9vvY4ZM0/jkpnX093eqh6eLxpq6KqrI+mah7/SOHU2xvY1qzltjfn+qEBr+4o7TyHS20RZyzFsKm9UhmWKA7/VLjyk98PTecISTxbvMNMdbbn+Tl5sb85EopLTVYdlMWzhynwF7SNz1f7yPCW2bhguS5dsKQOfMzaR85YsorKimSWnZHLfW7uVkZSmpRKmmO5JVnFrCZPYobe7iuvtIjKgz7yuslHFlMHsRj03P81cmSw9VU1y1S2dSn1vxfnHUxYohQ82XTX/F+lRgS69uvefNUw43Sq8pBvNuICWshhmXZRIv1ZyDjriertMCdVe530PNXyNTXRFOX+2//P9AA1+v//0cN83DONmwKn10nNAjLZdFKbt+0zfyO/3v41mgA3D+AmwxzCMGL/fPyT99waCYWOUDwbhEn3hDHW4ZqxON+vBGmbDMDjrnHMpmXYqv37kF5w790x+uORHXHT2LI7KSSUyZyLxxUNTcOLEX26pKqOlCuKmvcSfX3iIi0Y1kXlBPnMvfQBfRxujxhfxwE9nm9KgWMMc0vljZmEam8obVcGDwM7MSIt3Eemto31krupaba1OM5XucGWT463DTfCht3t3OsTA6+EFMeZ6B+o5YxNZXW4WfHhGxTElqol7Z0+gsTiHq/7wIS8+slJ9394AVYSW5NyzXD4ata4wAlXcEROQvvQFC1EqA1rJ41MTWLlxJ8v2BKsYE1JS1ISwHRifegObyhupf2+LqgQU3W0IlqrLPfpKYIyf1ddyXNpIjOS0kPCChYMcxpj2JTykX28dhyvrwu/3LwOW2d83DONyzLiyYAKwy25oDcOYDXT7/f5NgbdGAF8C3YdmxM4YVom+g0U4Wttgbi67qJGTkIvT9uH4zW63m1tvv5NfL1/J+nV/ZPrc83j+fzewr9d0FXoSUulub+WNmtY+RY36Q19CR6d95wZKfttCWrxLxTtbqspYV9nImpoDqvdbY4ePogy3Jb45szCNuflpzC9MZ8kpmSZnOIC5+eZnEocUSJcTgax6Gjt81LqyqXVl84nPRWdErFKsE8qeVBjqqG7pVAZST+bZIZ67v7WBjHdWkRbvCjHCYvz0kIz0w4NQw6RvV1bfzo5Pg+pqme5oFhfncPOCE5VGR1vAIHsbdqqkog7ZxtfRhre52ZIgnJufpkSCJAmZ6Y7mWHcE3V2dZKYFE412w9wfBmOQ5RoPgxiyHS8BxYZhFAeYGMuA5x22SwRWGoYx3jCMBOBB4AW/3/+1VsoYUqJ5OMMwDP9e7+dDsq+ByBH216VXFyIKt4++ju203fa//pXHfv0Ie1tb+cHVV3Pm7LlERgbH4N5XMySl2nbPWdD5+kPcVxa0fKKoJiEKiWtKskn3ikXaUlorCUTusichlbdbUXFpaT8lOhhvt8JFt/4PgOrEYe+OAuZEcEtRMpHeJmpdZteQ+uZOFp0+jqIMt8VDdu+rwd/aoApjcnx17Ht5laocrP/mQi55ajuV69cDcP0tV6hjyQrgliKrrKfQAEVLuijDbdGLFs/aXvFm76soE5PEjsEUlJozNpGzHiuzlGufXDqfRaePU6uM6pZOnnjGFNe/8sxUtj6/gj/df21IHHmggkJOAkThNDAETivFpISj8fv9xoAO6gDDMFZs/PH3Fp2Y7Tzuts/3M+6mx/7cV/iin/2fhRkjHgX8HlgUiC0vBG73+/2ewHb3AD8ADGAjcE2AsfG1YdiELwZrAMNBN6YHW26tVwM6ja2/7znh5G99i6e/+Vv+WvY2j/3qER5fsZzF11zL6WecSQIH6ElIxd9HR5OBQtdo1rGm5gAXv3o3PUt/41jw0R96ElLJ8dapvwElamSed5cyKlVNXlZjcqOPbdgGCaENVyXWak8mdkaYYv45vjrFRMh0R1s85ITqLXQ31BBVUEJE7bsci9mbxz15BmBS8DLeWcVfrirlvsBkI5NMfXOnMshCIRTqnoxdDL+dmaJ77nroQ49HS9NZoeGBycxY9tz7sOBEZhamsX1NcJ+6Ah5Yy6yb66o5YcJYyxjCecn2e9WJJmdvHRUOsq/+nJfDCX6//4/AHx3eXwWs0l7fBtz2NQ4tBMPGKNvxVQS4+6LHQWj33v72O1gho3DbGYZB0aRT+FbRJN7etpXHfv0Iv1nxGDdfv4RTpxbTmzOxz1ZTg4FunF3xiab3tuAhZq+9m+IVr3N2zkhOWP5zal0nOwr7gLUrdCexMDLXIm4jxR7Sydru+a4GlpxSwjffeJrXH1moyrNveng9LVVlVAbKnSUEIMcWj/yWIh+3H/cF/lZTrwMCKnUNZtWgxFLtnmOUxKq9Tdx+HGztTlXFI8L/7YyIxR3YLqL2XdLSJ6uO4ONTE9gb8JIhmHhb++pOpQq3dXyRSvBVt5iNUPXEaF1FtRqPr6ON+ve2sAyTRnhy6Xy2r1nNyaWmqFVpbjQRxod80VBD6XmlvB3guq/7+U2MT0nki4YaopLTLYY2nLesjPMgwxzhtC6Gk2EeLhhWRnkgRSCH4phfVTku3D7lbwg9l1OmFDNp8hS2vP4XfvjjH/FB2ZscddRR9OZMHFLVue7yp3ju/XrA9PTa5t3O5Oxs6r+5kNrANjoLQSDem2NiNJDh15NxQr2yd15+ZVcbFxWUkOOro9GdytxLH7B8LqESXT1NIEnCjAZTbjPInJjIlKjQogkIpYi1j8xl+R8+BAJx5YIUxUSxNxuVar61nGtpaKpDVOESUlKorGimsqJZGeLEMdZCkIRAZxHp2RfUuwZK5zOzMI20eBcRtdtCjgOw6x+7mZ2TOGgRIrD25LNv55QAP4KvD8PGKDsZRHviTf7vy3geTBhkMIZ5oMa7v9CH7GfO1CJ+NnoUf978OsVnBBLII3NxD6FhlthoVZOXRk8yt7aezIuzlgDBmKYs14XzKzKgArtA1N79PcpI6scBLF2lPaPilOd782+3q4SjKz6R/NmzwzY0XV3eoLzuP159KXG9XSoODoAn1dRgrjDjr6K3IbTDEYDXU8Ijb+2mvjnQ6Tt9HONTE9QktBeRHzX5wxL/rX9vC5urx6mxSaz85gUnBrxiD3UV1cFu5cDsy+apkEZjh4+bA8PMLvCoGLYUn6wmWP6d5fIFVgEQF4iPL3/zI/x+P7vq6vGkzgj5PRUVLjAphbsf5b52MshOHrCTOtwRL3noMWyMcn8YTIXfwcSSdSM6kAlisPFp/bv6+50Rsdx161Juvus+PP+7np/ccBNZ2dm0j8wlMWRvB4fI+3/ARdcvpfS8HCK9TezOT2N9wHtr22NyaTPd+UxKhqSxoUcV1oMYTjG+dg3jpJhIVXgiYQJBWX27iqeKQc5ICfbX0xXnxCCLV7l3fw9uX5MlGQmwmRHM/+ZCM2wFNQwAACAASURBVKFY+y7dFVuUcE8sppGb7klmU3mj0joWFolwiYX6B8GqvIyTSthU3khVSpxZRBPoai3KcgBr9wRj4QkpKWqsWS6zYlD2KV6zZ1ScUoiTRGJavItPfKiOJzpaPniDxMRjSIo/GrBSCHUq3ED0XpwKpPqqA7DXCgzVirWzoYX2nl7Hz7z7/6WVz18r/s8YZbB6tPLaaRv7ZwejhTGQcQxVuKNwygxefmUKzz37DPMvnMess8/j4h9czY5Pu5iaG14NbqCo3biL2o2XkXP6WDLu/BWZ3aaBqW/uDMQ+U9jdfoApUZ+REGA06OdoZxeIuL0de/f3mF5pczOVmFV19hDA0geCesTiTQtEQlO6WROI2YJpgG4pMhOW+vckoZiTnE7XttdorzGTmO01DWSm55LnKSEjJY7ta8wwQmOHj7L6A0ris22P+S+/IIX82bMtceH65k5LZ5HKimY2pyYwvzDd9OID22WkxKkxzRl7vDofMcj1zZ2so1HFzsVLlpi1MDqkCu/zPbv5x5+eZt3zTxMVuU/tz6l7tVx3J21mHRZ+s82YhxjfI12sDyn+TxllGBh9R9CXjkZfHvFAk38HA3uoRP6PinJx+fd/wDnnfpsf//h6Vq/+LfmzS4ekTPvUV59ia3cqC35fSeVZZkx39mXzWHT6ODh9HBeN+ozuipf55LltpM6YTFRyuirRhh4mJYsYvunNSNhifkDrGFAFI4tOH8dKTHqbrsYmxvmmh02K2gM//f/tnX18VOWZ978nEYaEyUzIC2QyaRIyA0UriSh2I4Fo8XWBtrZWWV1ErbZVn3btuk9bq67bF9dq292tfSzSrltdtXbBbbVdoGrBFxCbrVYE3MpCEkPMJIG8kExiYJDkPH/c577nPidnJpMXlcD8Ph8+JDNzzpy5c+Z3rvO7rut3LVMRo4ySW/tirH0unkxrqQpTvcyvEo3+ngauNCJUL6tR71uc46HME8OMRIYZ6RsFQboOD6qa5VhfNyuuu1fpwiVn1armDwlnhFu5bJl6/OVn32DzDq+KtiWB62Ordi+pULKP1JflPvVhrfLYJXYfHFC/L5rSRtmKFYTa/wSanuzeSm1HKlFtZrTN1sgz0j4mYd3ycY0ThpQTZYETkWgqBkcjka+zS3AiE4H6++v7Ligs5LwLL+H1N3ZSOSHvJpJdz7/STNPOehX1tbT3U7pkGt6vXskLjT184pmfk3vudRwGMtXxiDUUJkOCBN0iZp1cqkv8lF5eyfyZ2WqGoER9R7821WMZ82dmc06BXJNMWq0eGt3Yp8wTE5UfxDvXKqJNzI5at/MxICZK4pwY9AV4vC5ia+SQxkSyiePmLy9SGrOsHpaJwNuf3AXACzdVsq7hCE/dv9bmGKdDvodTGy8psndByunZ8nzU11b+39fWxMeGOhGeOSQd/QTDdd9Ew1H1JCGIqhO9/nmku8s0JgaTmpR10nQj5GQkm4jAR9qH2+tGe4KORcfWt/nYnBCbN21wzf6PBXk52YJorq1l8w4xJeOeyyv5yAN/Q+BLl3LvlPNZ9qnv48nJo+SsWq64ZK6Kcjfu6+aex14n2t6uzHOc7dbKXMgTYHefjKQLWFwQJKNRlLPd/qRIoq35kbC4XRmaBo6L4vyZ2dxzeSU3HriUaHu7iORBGRcps35POfnhkHocrAoKh0Xo/piH9c/stew3hUGQlCa6D/RzxSVzWTy1DbM+gr8gyC2LQuoiLn03Soq87I95WPucIOhoezv5WZk8cX4WT1SeqeqWS4rOUJNMpCwBcac5GSGrsVce+4VPr2J5pyXCotNnjTi12i0/IRN7zsoUW2QtSxu15/szsm1acpqg3z9MSlJORLaQOOE3nih2JBJ9P/ftfC1A2exyWlv2j3mShhvODYnMv4wCT//dfex4bh9lt35T+GZpkFUaj+/otGm+3QdEt9nii89Qfg/SbEf6X1SX+AkXCo/ll/sCLHZI4rI7cJA4ScjSOiljXHHJXPa0FVHqn6bKufSIMpV16b7iLlr7YoqE77lc3Hes2dboGGDaRvOjj+IPBfHXnI+3YqGKzGVi8fEdEaFLI5J6vvqtNAZrWPvcq1ywoFj5aMjkHQy3/XQOSN0f8wxL8MmJLs0tLZSe99Gkn0+PhlMhTbdzcLQt22lMDMZNyoZhXAN8GygA/gR80TTN/zUM42LgAaAY2Ahcb5pmn2EYGYj8y8XAPaZp/lOq75VMy7JZDjJ2oky0vZP4x7of52OpJAWdcsbMmbN49913qW/vInv68GnOY8WcmTmK2HobIsyoyKU3NwQ0K3c5iefrO3noka1EI3upXLaMvFlh1R68y6rRjba3032giI2VxcrKcvHFZ4gpJNbUk+IF5ZQNDaiot8wTgxhKr9Y9hWUCTF4IinM8mJEI/bkhwF6q1XV4EDvNxZFz9Te4+SnRon2PJaWoqHpJhWp0KfVPY9AXIHB+Ddu/8Qgt/7CBVZv/hd2+hTT3HlGauT615JZFpQwNDXDmJ79NrK+bV9eJssLbnt1ENLLX1gxj61ZUbefxiTC6vgyi9d00h2iPtFJSkGub2qJD2qEajoaSZM0kEvJcdKvK8MprnXuBRBoThHGRsmEYc4CfAJcDLyLGq/yHYRhLSTx+5ULgLeAGYL1hGD91GE0nRCptzaMhYzcydFZwJNpupPdyq22eqOShAZSWljLY3QbT5/BSQ+eEVGGAiJhfaugkcH4NpatDbD84oKZhSKyoLKbUP43NVWGaiCftPmUls2S52y6EHFDqn2bNqRPbS8c1ELfCmT1iOnXX4UEGtz1KZlUtdS29KjJeGi5QkWtxjseWeIs6Br3qaPSUk58hbsLdCLqkyKukFDlE9pwCmD8zj93We2VGm8isquXMuuv4hEXcxZZMIklUTlhZtSCoLEcrly3j1XWPUzivmu4D/dz7tWXUd9Ty0CNbecrySpbbyX31Z2Tz+I5mRdibgbWgLlglRV72/fEP5OcXQFsrJJgALuF02nNrJnFzT5TQG0zk9k6k6rGRRuoYb6S8FHjBNM3fARiG8UPgH9HGr1iP34uIlr+K4JRM670N3HMwSZFIz0q1aWSkmuZUa55HIlw3gk+luSWVC0tRUYCOAwf46GmixGrfwT7mzHSdBTlqFOd4mFpVy6AvQHOD0DLlrbo0EurPyBZGOQuKleSx/TsXKKlBN4RfPLUNrPrdeQEf/RnZ1ES3ck5oIYMvPUxPUxO5n16NH+GROOgLUP/nTtvx6Ak/GaHK8rX4mKrEskVvbgh/lTUt21pf6XMRJ2YRMfs6I8JXOhY3U1rzqzdZUVnM8txsS1YQhF3mC3DFJaKRJD8rUyQWEcm/q4tu5OVn3xAWnVefyaoFQb53m+6VXA2IkU+Z0TZ2Hw3YjKCkq5wk5lhvJ52//ylrf/Jj2PssA01NZCP8JUdK9jmR6mDh0dT/pzExGC8pPw08q/1+FjBAkvErCOel64C3ge+Ypulq/2YYxheBL450AKk0giTSmN0eTzUKdxJtKtskw2gbWrKzszly5P35sujkIpOJegdfZk8bXl9AVFJYz8sxSvNnZquklGywWPmZCrA4dk9b1Jp0UouvfiuDQHZ5ua3zbghskbG8CIgmlfhnlpUdMmnr1nEm0XV4kC4rAbj74IAaTlpSJDrtinM8eIcGlLOcdIWjQ3yWeQGfLbEq12h/zKMSm3J7OZgW4kb19R39w3Tjjj11lFetYkVlMWbnmxQHy9VzebO85M0KQ1WYGy+aS3F2Jgd+cz8XfO4qLj51Fj1WkYpOzG5wVlRIJNKKdbJWa2nVJTvvNiba8D76ziEO9btrIz1HP1BL4w8V4yJl0zQPyJ8Nw7gMcbd1G1BFgvErllSxMoV9/wwhe2AYRtJoOpWuJXAnzxFv31JMknzQ0UT29OkcORz36G7ti01YpAyW6U8Mil12OegLCAe1YI3rtnJ+n6zNFesbXx856aQGhmmi0mqzukSXCIZ3DUK8Bpmh+N9YErObfaaAh+beI6rsraW9HyrFdhn1r/FepIHD4Vqae7uVgdK8gE8Z5evVE6X+AM0d8UoKQPlA47g46NNdnKgu8WPEgtS19Ko1y5vlVZakAD++725KZhWy5utfxGx63ba9JGYJuabOuuWRknaSZJ1BgVzPdOLvg8GIpJxkxMq/m6Z5vWEYBcC/IaLk603T/K1hGD8ghfErEwXngMeRkEjyGEn7dT6e6rYjjbByIpXIIysri8MWKbsZu48HG/d1q1Iu52SR6hI/Xo/4wjslA+/QgKrX9eTkcesdN7BqQdA28062Ja8p8lJTNZw49CoKAY+arJ1vRWxSHqku8SuS0LvQZMna8/Wdopsv1kSNVbc86AmQPyePNT/6qnJvW/vcXqpLzkZ+Ul/9VpbPqYU5eew+OMDtT+7i1XWP031AzPHbtWmTmtIivStkZ2HcGU+sXeG8ahUtN/ceUVNFQBgSXbCgmDJPjN6sEGt/Uaeea0FEOKWXV/LOH7fwp5e38NTfXYm5/Ul6rVZxCb0pRnp8SCSy5BzJe9k7NGDr3JPuf7pOLbdNyxsTixFJOdGIFQDDMKYDLwOvAPO0hN0eUhi/8kFjIiLaVBJyyXTuZNLEaG4Ds7KyOTww8V8G6RiXKpwt1aX+aXhyhD+GXsssp3HcqE38iIZrFYHJiNht/5KYE0G2F3sT3E7r8+kkrio8RO+ckK2kzygIKltPmbCriUYoKcpjl/WZSoq87EL4YMhqFDlEtvrzZ1PmGaDLulAtDRewuSqMr0joxqX+aZbuvkg1noQLva5/d+nFMXSonfu+cxf/UDyTd//wJ04JjU47huSm926Py9fLNdBfM1rtOo3RY7ya8l8DbaZpOsdZPA38wDCMJQg9OdH4lZSRiEyTmacker2+PydJOkvr5DapJhTdnnNO4B5teZ0bsrOz6e8X10C3Cc5jwT88u0fpozJCls5lknzLPDEVMW3smKFKt5bPySOzp43FU8UUDxBG9I2UKzlj1YKgVZlRy7yAz2bt2Wpp1hWxBvJnhobJFWWeeAS/olIkF/OzMhn0BMjojNg0acK1FOfEtV6pET9f12klKoX26+uM8OBlonpDlsQNOxd8AZ4oaOPlJbepY7n54dtUtL7+mb00vPg0LTl51F00l4rCQ5RZJFbmEQk/pxTQ3HvE1sknW6hlC7rEjRfNpXHjj1k45RTOrPwI/lBwWKv4QFOTkC/Ky21RciItWSJRV5/+mJQrkkkWo5lykkZqGC8pVwLnWnqxjhBwDcLRX45f+d4438sVE0FwqSTqxrLvRFH1RETsWVlZHDx4UP0+EY0kksSqS/yqoqHnrhsoXb2afGtitPyCbvctpN7yuKjv6Ge3fxoQoDjHwy2LxEXo6EsPw8fL1f7zszJtcoi0vASUQ1p1STlYhCw/k3RLq+/oVBqw8/MOWBUcskZXDnwt88TYH/NQnIOqkNh/2MNsa7uN+0TL9lWFgmC8DoLZH/PQejTAmm2Nqvb6ibs/y1WFh3iCGap9OtYnNGgKGVYbrJ8HToc7fVrJvIBPVbmAGJZ6X3OEv7jgbAJLz3atSfYHQwnrlXWMhjydgUui7eTnTOvME4vxJvq+DHw5wdP7cRm/MlakQmSpGKUk87rQt5lILws3jJeY+/r7mD59etKKg9Hi6jNL6O6L3ym03PU3gBif5Ldu/5VmelRE5pJg9KGqD152Or76rRhVIgqVdc1dhwdVskuWfu1xTMQGbP7Jsrqi6/Cg2r/sbLvrtPc4unMrfU1N+GvOpzc3hNcXwFcvouauYA37YyIxV13iZ7mlETf3HiF/jrjIbHiqUUkPsyPbGdy5VZUDSn1bErKcPv18fS2EA5T60bw6xFr0LjpdrR/Yz8GbfvWmMj+S5YLSyjTa3s4uq275xovi7nn1bV385aKLhmnF8uID7om9RAb/I2E053w6Qn5/MKnarN2ILFnnndsXwyklJMP7TczO90jl/eRnemd/Mws/fjYQ/0zdfQPk5YzvePNysnns9RZu+8EmfEV/xc5vLQKgMeahrqEXODKsqQMEUaoJ0ZcJYmr0lNuMimSrtF6F4PRcdvonV8SaGPQE6MLDzUsqVCdhx546uPfr3EZ89p5KRgG/NE/nnu+9oHw6ZJWGxO6DA8yfmS0SdJVCajGBti3bCQBTq2rJzw3R2qcnO4U/xvpn9rKevVRWFXHrHTfw0CNbKa8Kc/eyU9m4r1sNV5W11V2HB7n256/aRkDtaStSn100kAh9umlnPWsRbd9DQ0Psa2pmTmk8epaQhv3y50RwG4iqPzfS+ZZ0pNQH8P04GTGpSBnG1vgxlpMnUWef89ZuPCflSN2DifRugHfeaeYzn/vcsG2O9nYy1T++7j45I69jD/xj3VyLRGO2EjFZiSEj2Y3a9l2HB+kK1qjSMUm0EpKY9fI2nZxli7OEHu2GC72qmmFPWxQK4qSU0fgaJvB2sIb6HRFa/rSVzbO83LKolMye4bfvXYcHVcnZy33TwBeg+Js1TLWmcAPc+MAr3H71mYQLvWrenmwvz5u1jHkBH0/c/VnlJKcPV4VpgIjMuw/026JqOEN5NstqDpkg7Zo1i+c2b+Zb6/6Vj4ZmM3OGOEZnhx4wTEOWGK3W66xgcvvOpHInOh70tfZx6JB79Wv0mFMhPXExqUjZrS8/GXHB8ef1mqisLlGCUYf+uZub91NaWjZs3xMBSXqSJJzQZYSl4QJa+4QOLYyCLHP5HRElUUjnOBn9Sm8HXVuVJC9JUk7UXrWgnLqWXqW7rgxNY+WL32d/zEPe+u/w51+9QfnFTSoBdvjc66jb1y2i+B99leVz8sh66WHMYEiQrqZT674aujzhycmj5xfXUFQrlLnrX3ya3//n9wXxlvgJF4qWabsJv4iM5V2A1Iqlv8XtV58JV8dd42Tzim5DGuvrZtq0qUzf+SAvvDbAnTf+NRfVnoNhGIAgZTdidktQyiSdW8JP7iMz2ga58aSqXBewu8KB+zBh537TmBhMGlJO1tgx3o69RKZAo9V93QjVbZ+j2Z+rV8a77/Jufz+FM2cOe1+YmGgZoOSsWhX1udmEhgu93P7kLkW6OiGHC71sRpSQScKtiDXRmxWi1D/NNXp2NofoFqC67jy4bR2zgyHeKy+n/GJhnpRtDXvN1/a5MjSNwZceVs0VUwuC5OeGVL2z9GcGISG0WOOefMG5mJ0RVWNcOK/apnHLi4f0V5bHebWlTwO2hJ3Ecm2U1q6d7Y7IWSAwvYclZ57JfTddwSmn2KuKdMlC/p6sA885XRziWrQkZn9PA13WjMRE0AlZEr2swDnqsENNY/yYNKTsBrdOPGcUOlJkOlJ1RbJIdqTjGg0BJ7uIOI9/T0sTJR8pxTCMhMczEROvpVmOlBN0kly1IMi1P3+VXZs20X1ATOiQmnJLez97irzKgU0QQ5MqQzunYiHzZ5YO67iTCbW8WV4e+fzZNt9iQFl19p97HS2HB8kP12KcC2U9DTR6yrlz01vMC/i4ZVEp/p4GzMYIg0DTs28wIxwhAPiCEUVuH480sLiqFk7LYtBXATcJ+86+x+7j8Qv+hX8v8jLlt7/jxgde4frr7yJ03qXK50KugbyQyAYTEJF2SZGY17dmW5yodx8cUJHyb7/5Ce7cVMTLz74BCMvPWy+byz994TPc9sObhxGyhDx2JyHrcA6xle3ROqHrv1cUQL5lyAQxMnvsDTlOYpc/GwXBhO3daYwdk5qUJUbrG6FvM9pEW7KW7dEi1WN1vu6d5mZKy8rU8TgvMOr2dZzHJ6csSxKS0bKUGPTRToCypJRYs60RwJriXM5sIrwdrKGuoZfqElHz/Adt4LXsitM/t/S3kN4bvQjzeq8voBJ3vbkhWg8O2I5VTq0eaGpi9ystlDT2cKi+gxnhQvxWA0bup1fz8tGAqDn+syjPu6rwENnl5dSuXkDJt35M1bdeUbIGxD055s/M5pM/qVN6MEDovLhR/orKYp6v71Q2pdUlfrwZxzjNaOe1P9Zxx1PfZsf/7MGIHcXAYMCA7/6nyaXnVVNgDgAzAHt7tM2wX0MqybrMaFvChKDZGXH1tUjUhGPbb9J3Pv5gGMY24GumadYleN7VcvgDPMTJR8oJb+mTSA2pOLONZ//O14w3K51MIwepJ5eOeBxjwdQF8T4gWcoGqMoJ3YBo+Zw8qkvOVvrv0nABS8MFKjps2llv82CuKBT+Dmuf2wsXzaUsNM02UUPXlEEavYuvfZkH+on7JA9uWwdA5pKVKjLUqzqMgqCaNFK7eoEi5EP1HcqwvtFTzpotb6kZenmzvGImYVUtpcEQv2w4YhsVBdq4JrCNzwJhWfrE+Vk0esqVpu6PdRHb+Xu+XvcH3vzfBirCc1h49tmcddGNPHDGafiH+hjqjHdRnpKZOSx55+Y5IbsY9Wg5URNVf0b2sPprsEe8evQtzz9dDvFao7acF4jJ4odhke1ngMVJXjODxJbDHxgmHSnD6IjTWeEw3gaTiezzT/QZRnrNO83NnPaxj9le4/a53dN0iaETMtjrhSUZy0SQnLHnLwhyR3VAJYYe3xFRt+tyAsnmHa3UB0RjBaBajjOjbZT6A7b9SmKRskVrn3hudx/Mn4kagjq1qlaZJuVnZZKfFV+DwZceZv+W7ZSuXk1uVS05vgAl1mcqswhEv5UvKfKqDrv6jn4aS8opiWxl+bm11F9by562M5Qhfl1LL3daEsS9X1vGhl1n8PKzb7DYmqz9RAdAL0tKfbz9+1/yb9/8FX956WV87gu38KXQx8jKnq7etzfLwylZAbx5JQnN6OXf1ilVKH3ZxYJTJ1UnUTvvphS0FnVJxrqJk9djbZsbsr2P27Edp1iM4Ltk3u3JLIc/MExKUk6GkchaJ/SJkCLk/hINbnXuf7Qt2m7Y39TExX+5bEzbjhayjVpCms/na2Y1MovvxN3LTqW1L6aqMAZ9Aao9cVKnB1VKJgkgPytbtSJDPAEo0Z+Rjc/aF7G4ZahsGAHY+o1HWHDTecNu1yWpAOJ/TXuV0kd9Rz+tfTFmB0O0aM93H+hnxXX32iLjl+dV88Tdn+WJ87MY9AX4x7pO+jraiLz1Oo3PP8VH54T42X88xbHsGWob50VOHlci3w71/DiDgWQXe7d9O9v2bdUYWeM/ng8apmn+PYBhGBckeVlCy+FUB3FMBCYNKY+XdJKd2GM56Z3bjMZ/Y6yQ79fU9DazKypcn5MYb/VF6LxLbSOPIO7OVt/RT3Ovl+qSGso8MdY1HKH+z80sDRewakFQkbhslX7k82crT+QyzwBloWlkWl4T3iHpRhZvm27ti9kuBrIWWnbjLZ9Ti7+ngVZLD54/MxujICgmjWRlUnPftUwJhoY5oenkL8lRXjgkls/Jw1e/le2+hdz4vRdserITHXvquOqOIZaebpDb8RZ//MN2YrGjLPqLj3PdV67hwiXV9OYGbZGn845DIk567mVobueovOikcu46nQr1IGIYQTv+HiMFGx9g2WnQMIzXtN9/Zln8ThR8JLAcJnmEPaGYNKQ8EUim+Y6GmPVIe6w69mgh3ycajXL43XcJFfowXN57IkrhnJg/M1vJCbK2WJW0lfiBI7bXx6cyCyI6x3FIGY2vMVix0LZ++q2yNBOSCTXv0AAMxYnMOzRAo6ecYos35D4keRw+9zqm9jSosi0s8pJWo5L097RFWfvcXuVhDFY9Maez9sldSQkZIPPYu2T++RfUd0znqssu5ebLLqCstwnDMJgS/Ej82KzoXydiN6LTW+aTXeRTlQv0fej7lhcnKQ3pr1V/kyx7nbITdt15Ytr8D3YdxpuAkfqHBgEipmlelGj7kWyGUziEHj5Ay+FEmLSkPNardCrEPBE1yxMN+f4Nbzcxu7xMNRTYSpXGUQLn1JOdKM7xqOhSb/qQcoZsDAFs9cZu9c3y83QdHlRffr0+ef7MbBWxEo3X4wpfZYZNrs6MNtHzm0fJ/fRqVRq3orKYz7X+RvlCSLMhr0dIJGu2RYc1cHhy8pRHbSJC9uTk4SueA62v4jmwi4uuvIG7//ZL+Dhi/S3iDT26abzTm1hWjjjPM9lNp7eEu5V06tBL1pIRthvRO61X9fdwvl7/3X2AwIeLZDbDKeK4sByeNKScKPJ06+hLpaxttFJGqhcBN/06Fe062efT3/et/W2EZs8e9rrxwEnIhfOqueKSuZZ2nIm/p0GUTJWUqwhZbyrRRzJ1HR60mROtqCzmnIJpihQzo20MViy0EZX+ZZdjmQa3raO3qYnehgiB82tspjsViLpahZjwv+j5zaPkX/NtqwTPz9TCWltCLzPaxtGdW/FXCevQlqow3UVFylgeoOVPW226sXNdVq6Yy94nf8zR6TE+/9PHCHyknEMxk4ysbJuu7pZHcDPDcibN3KC/xi16lVOmZSWEW6WFfm45hxMkispTHc4wEQ6Fxwkm3HJ4LJg0pAyJSc35eKLEmn6SjUSubs0lo5U3UkEysnerqHi7sZGKinL1mN5dxRgiZbcIubwqrEx8Muq3ctSyhqwoEG3PssKiPiAIWpbLyRl+EvI1+0v8VFjH36VVTMgIb9Ca9yeSdpkwJAyBpgRDZJc3xDvyLPc2ENNBhioWAta0D085ZVcvZL/lZyFn6MnxTPKWfb7VeLI0HFP+G2tneWnaWT+s7VmHJyePyy4sZfN9X2H1ddez4q+u5cCA8GOQRvw2ZNkvxLp04Pa8299aIqVSTMd0Eb3F2q2W3TY0IEHULuEcRCDnD54IMAxjNXCXaZph0zS7DMP4QCyHk2HSkPJo5QO3UriJTMYlq7YYC5JJJvrjTW83cvGy5cO2P2XBJaN+z0SSxavrHqfuornMNt4E4taQR3duZXYwwi2LalkaLuD2J3fxkFWLnDfLywULilm1IKjc4+oDwhujrqWXxzumAM0AlkH98Nv2/Kxs5W9c6g9QHCynrGIhuVXi1rw3QTpsaQAAGalJREFUN6TGPEE5t4TF+tz0K3GczkaXcwpEqd7Gfd3KZCl03qWWbixQ39FPSZGX7gNFrm3PEhdccRH/vfYO/u4bt7PowhW2W3ZdegEROepRsZ7ok54bssVb3mW4XZzldl6Pe4WLrsPjWE91V6L5OWdG25IOP3XzutA/Z7w9fWBEffx4hWma5Y7fH0WQsPz9v5hAy+GxYNKQMoyuhM0teh6L/pzs94mAG/Eme8+3324cVnkxHi3ZY/k9uOGX5umqmaO1LwY+EZkWW0krSWayHVuH3mgCqHIzvbxOj+a6Dg8qoyK5PYioTE7ykNUXcl+JzgMpqfyhM8bz9c3saYtSXhUGRIOHrL+WM/rk41DrqiVXLF5G34Yf8fELP0l59YXDNNREurmb7urcVkbZcoSUnNYt7z5AkKFXq1RxShh60s5WUeEsU8wV3ZBS0hmykq0Scv0TeZ1IOOWKifLzTkNgUpHySAb1E4mRCPjDiA6OHTvGO83NlJWVI+85M6NtMI6Ki1hfN6HzLrWR0f88ez+f+t4LgCArZ/u0TPKtqCxmRWWx6NBD6Mx1hXaC1pOAy+fkxaO2nriRjbTmlBURYDcjAkE2kmDvXnYqn2v9DYPbREeftNRcGi5Q1Rr7Y6jOwu4D/az98iI+fvpbTAl283bOqTxf38nmHa22yovKqiIqq25k08/XKyvN4JmLKW/9NRkzZnH2Z69T01KSkZZOvKnorc4yPd1bWq6DLg3plRTOKB3ck63y5+qScsoqRBStn+O7Dw7Y1t/ZOCQ/h7+nweYsdwLpyccNJhUpj1S3ORImqqtvpGaRkfYx1gi8u6sLn9/PtGnT6Ce1O4ZU4IwOH98RIW+W1zal2QkZra5/Jn7L331AGN1fcYkY+rl5RyslRV7uXnaqpU+/pnRgvUqgzBODEv8w5zgd3qEB1UZdEWuCKpHEM6JtVJeUW3KFIHuzM0JZxUJuXlKhIuKa6Gs0b9lOybdW0toZUy520XbLre2sWpp21ivbUhAXrL43fsfevv1c+e3/h2EYiiSfr4+bdugXLAmnJelooN9VSPJP5XzTCVoe3562qOqwBDEhW/5N0GQVeQcS94J2R6/V0deFx+acl8bEYVKRshPJ2qoTvR5GljJGQ9ofYOE8nZ2dFBRMXB3y0R0/B4Zry+uf2cvtV59piwZL/dPUF12Shrz1l5BVDJstQpdRqLxFz9emV+s6KgiiXbWgnDu1SE2itU+Uwi2e2sY51QHQPBsAVSqn2oAtPXXxVKAQTCNC86OPEji/xvp7Cbc2mdyDxCVwR5pfIzq1gF9vaSJvViclRV5FcvrwU7UGGgFKYnZ6FeufKxGc0bhaw1EQsyRk/W4AxMXzTuDmJRU4ofudQHzogLggDn9/OVFmItA88B7HMtw/34B58kgkk5qUYTgJj1R2lujxVKLfD7tWubOzg/yCwgnfr1NXrqwqsg30lEQjv9xyjlzYmiXX3HuEtc/tJRqca9uvTKjd/uQum+OcW5u22RmhJLKVxz6zctjxZUbbIGqPrqUL3JRgiIxO4T5356Y3mRfwWRO4y9XkbQB/KMhAUxM5S4QuLSWKkXDKsSgD0ytoePFpGoBdmvF/U3Au5VVhGznLn3X/Z6kPy9t9eY7lO/RhfWq4eN5+LnqHBugn27Zf5zQYN1nFScoyB6BLMc5OQ9lgI/F8fadK0IoLmzhuXfJIY2Iw6Ul5JCSrOx5vlDuREkZK79fZSX5+/sgvHAVq7n3e9nvovEtZUVnMhl2tw7RkJ6pL/Dy+I8KqBUFKL6/kduvxXZs2sQtY8aPUfFykBeTUgqCtN0zWFWckGBoqh4n2Onws3JBdXk7mkpWsazjCPY+9nhIhYw6SOXiYwczpI75Ur/zQyUwnLZ04ncQb70qM+xebkciwz+1DJOi6tBI8eQFIJJPo0bJbUlYiFRlCv4jIYQXOwbdpjA+ThpST1XEmIl6n8VCifUm4jbwZTwVGMvIdCzF3dXZSUFg4YdH6Y6+3cMGCYu65/DYtEeSnrqVXRccrKospXSKki/XP7CXa3k7erDDVJX4+dvEtADw0r5oX77+Sey6v5PYndynCW/vcXu65vFL5LUu9UibjbJ/fSh6J4aziOPJzQ/iC8UkZGVZ0nFlVK4gYq2XYagG/e9mpgL0G+ujOrUwJhoSrXMzDhl2Nw+w4E1egZGB4/Fx05XJWXngOECfZlvZ+16hYRp16dYe0BZXm/87SPZnEkyVr+kQPNxP5zGibqEixrE71Ujt93FVz7xH2tEVtLnjJoG+fiiRRnONR3ihuvc1pjA2ThpSdSCVpJ7/4o02uTaREMZEeGJ2dHVQEZ03Ivrr7Bqgu8XP99XfxPeAzt9zIY5+poD8jU0RcFwkp4qrCQzR6ygkXernikrls3iGki4pYE2evXMWuTZu44dpapevec3klV+2sBsSt/Jptjcq+E2ADrXZPCw1eD2A1fugRmZQp5P+yGURCjzp99VuVrWXP9i1WR6Agt7KKgOrk8xUVKXKWns+y1lon3cJdhYSMBlaGPiFK+EIB9seCrnqwXiERn+fndU1gSmloXsBHc6+X5XPyFCFDvDZcwm1AqjR5iktB8XWQQ2Hle+vJO/149Khd39Ypg+g+2s7a6rJ0nm9CMWlIeaT250RIhWBTrRH+sNHZ2clfVJ0GjE7fPvVvf6t+vuKSudyyqFQ0XWxqVI8/df9a+i97QBkPyZl6ErIaYOnllcJgKAovXXwULr6A9yJbGNwmiKQGaP7+OXznz1NU9YW8fe8+IIhOOrtBXKKQyGtqwnv1N9TtuW5WL+UK3e0NBAH6exo4+set7PxpPGF3qLGHlvZ+5td3UH7xGfiBO6oXcsui0mF10RKrFgRhQVD5Z7QcLCTa16OeH9y2jhJQ/sw2vIXoPiwvZ6CpiTzgyprzGapeqHIWstJBron8fyP2cjU3DDrsPZ3dgmBVsgyhmkn0kVUSbtUVuueG9AfRyVrfh/TSlpNd5NDaNCYGk4aUR/KGkEjUYJLMI2MsZWpj0ZLHmyjs6DhIYUHBmC4W0tPhn/+Ux9KHb2P+zGzbbXfhvGruf6VZRbSyHG5laBrE7GOQ9BpjWdWg67tyP1LHvGBBMTcvqbB9yeU6DPoCZC4Ryb3MaJuIiOVg1EgDPdqXXs2li8Wj47z13+H1B1+kpb2fuu7DVOdlqVv1lvZ+5i8qYUa4UMzpa4jgD20hu7ycEuCqqlp654TYuK9bkXNrX4yr7vw1HXvq2JSTx+ysJm688rOAiGL1CFYfZCoJqrchQm9DhEP1Hex+pQXYQO3qBQTOr6GiqpayggDFOUL71X1ENu9oZYO15svnxJs85N0BQEZnhCHpG4I4n+IdjgLhQivqltaoHhHJzp8ZT1A6a4ydg1FBdAR2WY56UoqR++86PIhfu1gONDUlOOvSGAsmDSmPBk7CTfa69xNu5DlWTbo1EqFx3z5Om/dR22tGmpOWU/t/8TmqIgA27useVtLmhsxoG/m5IdvtrNRzvb4ApatF4mmI+Bc6XOhlj1YSJ5s6eu66gbJbv0lvbkhVEcjIfP5M0bUmE35mZ4SBpiaann2D8ovBHwypSRvyFrvkj4+y/cEXeb6+m+6j9kSf1MQPNfYwIywqVvyhIDsefJEZFbmc9qVLMTsjZO3cyvJzr+N+i3Qu/NzX1T5ifd2829OELygMoH5pnq4SoOFCL9UeP/lhUbc71ZIXJCGDuLA9X98Nj+6wonVxgamoqqUsFGC/pd/rx1vqn8bugwOieqQiwBQE4UtMAVXr7Yb6jn7u7+hnaThAcSwuM+hOcsInI36BlJ2SQsoopyLWJF47wpRrid6GyMgvSiNlGKZpftjHMCIMwzBjPR3qd71P31nvKp/XoZe86ds4idsZyU5UUm8scH6mv//uPeRMn87Xb71l2GsTeSjL+uPCedWqzfiCBcWEC71s2NU6rCzsm/fGCWlpuIDFU0UbtOy4c5ZN6c5w8nep6cpZb/e/IvwuwoVerjTeJBquVds4u870BGDX4UHKPDEGt61TUbjZGaF3+xb8NefbotReSzuWZChlC4C9/UdZGs6j5r5rmWo1nOy865+Y//gv2R/zUBFr4ujOrUytqqX2Fx1qIjWAZ7qP3Jbf8N31L5B5yinc/NUfqU4/X3Auiy8+g5uXVKjjzmh8jfciDbRt2W77Oxyq7+BQY1wCmVGRy4xw4bA7DOea6Ovi1+5OAJuurndD6vMGL1hQbJUHemxVHjLCltAjYfm3P6cAm+QiXwfxQQBy3f2hIAVf+SGmaRqMEYZh3LU8s/DblZk5rs83Dx3mF8faHjVN85qxvsdkwQkRKadipznWfaVCtuONuEfavqOjk//a+Dt+v2F4g0MyU3uPVlPbfaCfKy6Zq5zRbl5Swa6ddq+HzTtaudFK8BXneGiknPyMTPKz4ome5t4jNrN53du4tQ/hj3EU1jzVyKafr8dn1fKyoJi3F9QgC/pkhKgTvYzm/MQNdTI1Zzh8AXILgkKD1nRmQE2oPlTfQcVFc6gAGp/bB+39tLT3M9DUxNSqWqLhWnz/VkNG43bKKhZCTLRqP9FwBIhf+AHMrr0MZQf43Z8PsqKymJKzaom2tysDphWVxeq4QcgZUwB/yK6z+kNNzNAuGjohAypir6iqpZV4Ik8a8OtmT2oCy77EJX2yG1PXjp3udLJaA+wVJWDp3EsqOKdAeG3kZ2WrNmyQ+ncN+dfUUiYvFl/5YcLjSREP1g32fLt4MIsMhnP7S8Jn/gfjfZPJgElFyjbntyx3okxUO6xvM5b27GSlbe83Hvr3x7j0k8spLLDXKI80ZWTZ569g18520Urc3g7MVVFRqX8aa7+8iAs1Uu4+EO/e0knTWSYlp4noTQRO34aXn32DWF+3alveU+Sl1Wo+kAQhj+OcAsho3M57kQZVpzwlGAKpIaPlCnwBFfFK+GvO571IA70NEWaECxVBz6gQJCij5N7cEDf96k1a2vt55PM1lBETk6fr4vtS5XGmSfbA2zD3LwVZVYpmGJnwlFOtwV6FYRQEyf30aiBuqzq1qpZcoFTTiJ3zA6cgyHlxATSWCHvU7gP9tPxpK9HgXNYf6LddNPW1luQ7L+BTteUyXyD/dnLiuP56t6oQKTmJZKzQpntzQzT3HlFy12Zgg1Xat2pBud0GdIwwTbNjgeGnnneZiz0R2cYRppKBaZpvjvuNJgEmjXzRFX0XGLt/RSK5YqR9jsbveCzHlWg/Er29Uc67ZAUbf72e4kCR7T3ychK/V3ffgKow0HVQvQNs1YIg1/78VV5d97iKqm+94wbbXD4ZUTnrVpVnMe5uaM29R1TZlzSd17+8MsLWb83dxtw71ycz2qZu3Z3S1eC2dbRt2W6LSEu+9WP+EM+DqUGt8lidHhHdB/qJRpp5b+cvmJJhUrb6bi48s2RYXbGUK3Ry1Y87kXOa85gzGsXIOWeTyFDFQvbHPFz781cBaLIsUkFU0OjHI+F0vpPJWqdHiRukXwnE5Qu51rsPDrBmW+MwucuTk0flsmU88vmzmTvLNy75AsAwjMJ8phz8LAFbtLyRA7RwZP7JQsqTKlIeDZzR7Xj03g+zNC7S2sqsmYWKkIXBuPjC57nIb8d2PEPU8piQgzr14aB6dNXaFxMeFeddGjfmsWDXjT3DImFbwkvrJJOEdU7BNFaG4t4KmdEmiNm1UOk7rE+YdmqnTkJOBWes+WdAkKROyHJ//lgbjVYSS16oZGT5Xk87x/Y9jnfuHEKf+iKfPis+5UVv9OjPyIZwrc2oX8JNh9WhX6CcEbN8rO+x+8gDXrjpG3ziwV22uurNO7yg6cUg/k7FOR7190mlWUQib5ZXReH6GK5BR0LRF5xrGwTgC86l+0C/LQofD9yi5ZMtSoYTmJRh5OaRkdqkU5r4kEJi0K0cL9nrdRQWFNDZ1WVLuhTneJgz0z0hst23EKxsuh6pyohqabhAfZlb+2JxF7iqIuAMRdaSfOQaLZ+Tx8Z93baIctfOdvJmeam3CAKErqxvK5FvEa/M9IPekBA//uKccsqI2chZEt6gL0BG42txzblHkLSUMgaB3O88RKOMhDvjxKj7M+dbbdnFOR7mz8wmy5rl905pkE/987381bU3MufcT6o5iE7IJFlxjvBBLktAyGAvewOt+89x5+BEdnk5bVu2k71tHf/1f67jkz+pI9oO0cheuouKhlmcSqK/ZVGpa6OIHglv3tFqi7z113aV+PE6yLg4x8PNSyosaeSM+B2FdZGYSO+LN4jOzGfKwTDTycDgdXpp4cjYDcMnISYdKY8laZdQeshKHkmPJ0JOxWQ/FT16akGQ3mgf7733HnUtfWzY1cq6az+e8PVX3flrbri2lnChd5gpDsSjPZ0wJTHLL7CevNI9FpbPybPpkDIaCxd6Kc7xKB1aRsuyzlU3vtHbgKW0AigjIRl1hQtnKL1ZIjPaJkZTIWp2TUR52LqOGeK4ppTDK83qAiHJWG97dh5H1ksPs+6aBwjOms7PsmOsrjmdWy9fQm+u0O+d0zi8QwPD1k8eG2ARdDyJ5uzo0/2KpXm9cx+6Xv7nnz7NjC3beWn1an550TI27DrDti+5v/qOfisRaJ8mLj+7/pnDhV7WOv6GIC7YrX0x6lriVRbeoQGtmUQ01si/6eYdXmU0NVHQo+UcTjnpomSYhKQs8WEl3xKVz71f75+ZmcmMGXnURw5S3zFos4Z0QpbAPfQI3Pu1ZbZoSieGeImUZ9jttRyWmtkjbsllKZwkonChldWvFK+X+5WErI7bqm9u7bNHUjI6laQmP49oNz7iEnXFB7N6fQHRZr1dNIBMCYboz8imvqNzWM11Mk9g3Qd4alUttau3s+HVBtqjx7h6XsAaripe69OaQwAygiH8BUG8uu5tzbgzOyMM7tzK7GCI/HCtrezMDV2HB+nS7DC7ZF1wsJz8cC2lwRA8+igPrflvun/0CtV5WTzxxnqu2nLYtnYSj4PtgiSJWurDxMTdRv6cPEr9lWoNwJ4LkH/TrhI//libMkKSEXRFrI35i0JJE4bjgYyWp5FJ5CSLkmESkzKMXK42kolRMvnCTWIYb0v3WMjaOzRAQWEhg31dLA2HE75O90SORvay9rkwTTvr1WMyGo5HUR4lSyhYnWD0iHl8LR9fzdrnhO2mNIyXWjKgDIr0GX3yizroC9BlSQRSPjmnAAa3PSoqK6x6ZRAZ/z1tUZaGC4aZ6ut+vaX+acwP16qyuN7tW8iONHDLudexNFyQcCqIlFPk4/HKESAGuZd9jkc2/z33X/9p/KGP2Aa0YlVQTIFhSUjn31OWxL0XaSAr0kBNMMTiwiCDoQD9GaU26UZ+LmHpKbaXk1IAqwSuhtmr4Qbg+z96hbruw9SVfpJH/utrZC5Zyf6Yhzs3vaXeX7+gyZ/rCr3kW74aOpy15tK/Yv7MPLqsc6TMExs2fBbiUtRozftThYyWoxw76aJkmETVFx/2MaSRRhpJsd85lDSNsWFSkPKHAcMwXjNNM3E/axopI72WE4f0Wp74yPiwDyCNNNJII4040qScRhpppHEcIU3KifGzD/sATiCk13LikF7LExxpTTmNNNJI4zhCOlJOI4000jiOkCblNNJII43jCCcVKRuGcY1hGE2GYfQbhvGSYRgftR7/mmEYLdbjLxqGMU/b5mLDMPYZhvGuYRjrDcPIsR7PMAzjCcMwugzD+LsP6zN9WDAM48uGYbQZhhE1DGODYRgB63HX9Ur2XHotE66l6/lqPZdeyxMUJw0pG4YxB/gJcBNQCDwD/IdhGH8B3AosAfKB/wbWWNvMAH4JfA0xK9MAvmvt8kLgLeAjwCcMw0jdlmuSwzCM04FvA5cCxUAf8P1k65VeS3ckWUvX89XaJr2WJzBOGlIGlgIvmKb5O9M0DwM/BKoA0/onW84zAWka+yngddM0nzZN8xBwL/BX1nOG9dpTrJ9PpoxpLbDBNM3/Nk2zH/gF8DGSr1d6Ld2RaC1dz1fDMPJIr+UJjZOJlJ8GvqL9fhYwALwO/BbYCxwBbgZut15TCezQtnkTmGVFH88BpwJvA8+Zpvnu+3r0xxf+FfgCgGEY+cBfA6+QfL3Sa+mORGuZ6HyNkl7LExqT2pBoNDBN84D82TCMy4C1wG3AucBngDOBfcD3EdHK2YAP2K/tI2YYxjHAZ0U1Kz+wD3AcwTTN9wAMw/gCom72COK2+RoSrBfptXRForVMdL6apnnMMIz0Wp7AOJkiZQzDKDAM4zfA/cD1pmk+gNDyHjNNc4d1Qt8GLLRuE3uALG37qYgL2aEP/uiPP5im+a9ANvANRGTXT+L1Sq9lEjjX0jCMKQnOV0iv5QmNk4aUDcOYDrwMdAHzTNP8rfWU00/zGGKIxWFgD3C69typwD5L4ztpYRjGHYZhrAKw1uJBIA9oIfF6pdfSBUnWcibu5yuk1/KExklDygitrs00zc9bEbHEBmCVYRhnGoaRBXwLeMY6wZ8GlhiGscTKeN8DPPFBH/hxiA7gNsMwKgzDmAb8X2A38AiJ1yu9lu5ItJbLcT9fIb2WJzZM0zwp/gEPIGZEHHP8KwOuA+qBXkTSb5a23ScRSZN+4GHA82F/lg/7H+JW+V+AdmvNfgeER1qv9FqmvpbJztf0Wp7Y/9LeF2mkkUYaxxFOJvkijTTSSOO4R5qU00gjjTSOI6RJOY000kjjOEKalNNII400jiOkSTmNNNJI4zhCmpTTSCONNI4jpEk5jTTSSOM4QpqU00gjjTSOI6RJOY000kjjOML/B4I/EDr7mW7MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "lon_offset = 25.\n",
    "\n",
    "diff_levels = numpy.arange(-1.25,1.26,0.125)\n",
    "\n",
    "fig = mp.figure(figsize=(5,3))\n",
    "ax = fig.add_subplot(111, projection=cartopy.crs.PlateCarree())\n",
    "ax.set_extent([273,329,-30,15], crs=cartopy.crs.PlateCarree())\n",
    "ax.coastlines(color='0.1')\n",
    "contour_plot = ax.contourf(xlong,xlat,wa_diffs_surf_to_700_map*1000., \\\n",
    "                             levels=diff_levels, extend='both', \\\n",
    "                             #vmin=diff_levels.min(),vmax=diff_levels.max(),\\\n",
    "                             cmap='RdBu')\n",
    "\n",
    "ax.text(s=\"$\\Delta$ w 1000-700 hPa layer\", x=0, y=1.01,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax.transAxes, fontsize=fontsize)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax_pos = ax.get_position()\n",
    "cbar = mp.colorbar(contour_plot)\n",
    "cbar.ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "cbar.set_ticks([-1.5,-1,-0.5,0,0.5,1.,1.5])\n",
    "\n",
    "ax.set_yticks([-20,-10,0,10])\n",
    "ax.set_xticks([-360+280,-360+300,-360+320])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_xticklabels(['$280$'+DEGREE_SIGN,'$300$'+DEGREE_SIGN,'$320$'+DEGREE_SIGN])\n",
    "ax.set_yticklabels(['$-20$'+DEGREE_SIGN,'$-10$'+DEGREE_SIGN,'$0$'+DEGREE_SIGN,'$10$'+DEGREE_SIGN])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.286169Z",
     "start_time": "2018-04-16T21:58:41.267Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_time_mean_h12to18 = numpy.mean(ptot_400_lat_subset[12:18,:,:], axis=0)\n",
    "ptot_400_time_lat_mean_h12to18 = numpy.mean(ptot_400_time_mean_h12to18, axis=1)\n",
    "\n",
    "w_diffs_time_mean_h12to18 = numpy.mean(w_diffs_lat_subset[12:18,:,:], axis=0)\n",
    "w_diffs_time_lat_mean_h12to18 = numpy.mean(w_diffs_time_mean_h12to18, axis=1)\n",
    "w_diffs_time_lat_mean_h12to18_destagger = 0.5*(w_diffs_time_lat_mean_h12to18[:-1,:]+w_diffs_time_lat_mean_h12to18[1:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.287546Z",
     "start_time": "2018-04-16T21:58:41.516Z"
    }
   },
   "outputs": [],
   "source": [
    "ptot_400_time_mean_h18to24 = numpy.mean(ptot_400_lat_subset[24:30,:,:], axis=0)\n",
    "ptot_400_time_lat_mean_h18to24 = numpy.mean(ptot_400_time_mean_h18to24, axis=1)\n",
    "\n",
    "w_diffs_time_mean_h18to24 = numpy.mean(w_diffs_lat_subset[24:30,:,:], axis=0)\n",
    "w_diffs_time_lat_mean_h18to24 = numpy.mean(w_diffs_time_mean_h18to24, axis=1)\n",
    "w_diffs_time_lat_mean_h18to24_destagger = 0.5*(w_diffs_time_lat_mean_h18to24[:-1,:]+w_diffs_time_lat_mean_h18to24[1:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.288978Z",
     "start_time": "2018-04-16T21:58:42.899Z"
    }
   },
   "outputs": [],
   "source": [
    "fig = mp.figure(figsize=(4,2.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "levels = numpy.arange(-1.25,1.26,0.25)\n",
    "\n",
    "contour_plot = ax.contourf(xx+360., ptot_400_time_lat_mean_h12to18/100., w_diffs_time_lat_mean_h12to18_destagger*1000., \\\n",
    "                           levels=levels, \\\n",
    "                           cmap='RdBu', \\\n",
    "                           extend='both')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylim(1000,100)\n",
    "ax.set_xlim(xlong.min()+65.+360.,xlong.max()-2+360.)\n",
    "\n",
    "ax.text(s='Mean $\\Delta$ vertical velocity (hours 12-18)',\\\n",
    "       x=0, y=1.02, \\\n",
    "       ha='left', va='bottom', \\\n",
    "       fontsize=fontsize, \\\n",
    "       transform=ax.transAxes)\n",
    "\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.axhline(y=860.,lw=1,ls='--',c='0.1')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax_pos = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_pos.x1+0.05, ax_pos.y0, 0.03, ax_pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks(levels[1::2])\n",
    "cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('mean_vertical_velocity_change_hours12to18.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.290463Z",
     "start_time": "2018-04-16T21:58:44.531Z"
    }
   },
   "outputs": [],
   "source": [
    "fig = mp.figure(figsize=(4,2.5))\n",
    "\n",
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "levels = numpy.arange(-1.25,1.26,0.25)\n",
    "\n",
    "contour_plot = ax.contourf(xx+360., ptot_400_time_lat_mean_h18to24/100., w_diffs_time_lat_mean_h18to24_destagger*1000., \\\n",
    "                           levels=levels, \\\n",
    "                           cmap='RdBu', \\\n",
    "                           extend='both')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylim(1000,100)\n",
    "ax.set_xlim(xlong.min()+65.+360.,xlong.max()-2+360.)\n",
    "\n",
    "ax.text(s='Mean $\\Delta$ vertical velocity (hours 18-24)',\\\n",
    "       x=0, y=1.02, \\\n",
    "       ha='left', va='bottom', \\\n",
    "       fontsize=fontsize, \\\n",
    "       transform=ax.transAxes)\n",
    "\n",
    "ax.set_xticks([280,300,320])\n",
    "ax.set_xticklabels(xticks_format)\n",
    "ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "ax.set_facecolor('0.1')\n",
    "ax.axhline(y=860.,lw=1,ls='--',c='0.1')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "ax_pos = ax.get_position()\n",
    "cbar_ax = fig.add_axes([ax_pos.x1+0.05, ax_pos.y0, 0.03, ax_pos.height])\n",
    "cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "cbar_ax.tick_params(labelsize=fontsize)\n",
    "cbar.set_ticks(levels[1::2])\n",
    "cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('mean_vertical_velocity_change_hours18to24.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# movie of w velocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-11T21:36:19.922275Z",
     "start_time": "2018-04-11T21:36:19.647834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x180 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xticks_deg_E=numpy.array((280,300,320))\n",
    "xticks = -(360-xticks_deg_E)\n",
    "xticks_format=[str(tick)+DEGREE_SIGN+'E' for tick in xticks_deg_E]\n",
    "\n",
    "\n",
    "\n",
    "levels = numpy.arange(-1.25,1.26,0.25)\n",
    "\n",
    "for t in [8]:#18range(72):\n",
    "    \n",
    "    fig = mp.figure(figsize=(4,2.5))\n",
    "    ax = fig.add_subplot(111)\n",
    "\n",
    "    contour_plot = ax.contourf(xx+360., ptot_400_lat_mean[t,:,:]/100., w_diffs_lat_mean_destagger[t,:,:]*1000., \\\n",
    "                               levels=levels, \\\n",
    "                               cmap='RdBu', \\\n",
    "                               extend='both')\n",
    "    ax.tick_params(labelsize=fontsize)\n",
    "    ax.set_ylim(1000,100)\n",
    "    ax.set_xlim(xlong.min()+65.+360.,xlong.max()-2+360.)\n",
    "\n",
    "    ax.text(s='$\\Delta$ vertical velocity (hour '+'{:02d}'.format(t+1)+')',\\\n",
    "           x=0, y=1.02, \\\n",
    "           ha='left', va='bottom', \\\n",
    "           fontsize=fontsize, \\\n",
    "           transform=ax.transAxes)\n",
    "\n",
    "    ax.set_xticks([280,300,320])\n",
    "    ax.set_xticklabels(xticks_format)\n",
    "    ax.set_ylabel('hPa', fontsize=fontsize)\n",
    "    ax.set_facecolor('0.1')\n",
    "    ax.axhline(y=860.,lw=1,ls='--',c='0.1')\n",
    "\n",
    "    fig.tight_layout()\n",
    "\n",
    "    ax_pos = ax.get_position()\n",
    "    cbar_ax = fig.add_axes([ax_pos.x1+0.05, ax_pos.y0, 0.03, ax_pos.height])\n",
    "    cbar = fig.colorbar(contour_plot, cax=cbar_ax)\n",
    "    cbar_ax.tick_params(labelsize=fontsize)\n",
    "    cbar.set_ticks(levels[1::2])\n",
    "    cbar.set_label('mm s$^{\\,-1}$', fontsize=fontsize)\n",
    "\n",
    "    #fig.savefig('mean_vertical_velocity_change_hours18to24.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# time mean climatology and differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.291739Z",
     "start_time": "2018-04-16T21:58:48.570Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize = 12\n",
    "lon_offset = 25.\n",
    "\n",
    "# RAINTOT\n",
    "\n",
    "levels = numpy.arange(-1,1.1,0.25)\n",
    "ax1 = fig.add_subplot(231, projection=cartopy.crs.PlateCarree())\n",
    "ax1.gridlines(ylocs=[0], color='0.25')\n",
    "ax1.coastlines(resolution='110m')\n",
    "ax1.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-11T21:47:20.683505Z",
     "start_time": "2018-04-11T21:46:21.769871Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "lon_offset = 25.\n",
    "\n",
    "diff_levels = numpy.arange(-1,1.1,0.2)\n",
    "\n",
    "t = 10\n",
    "for t in range(48):#\n",
    "    \n",
    "    fig = mp.figure(figsize=(5,3))\n",
    "    ax1 = fig.add_subplot(111, projection=cartopy.crs.PlateCarree())\n",
    "    ax1.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())\n",
    "    ax1.coastlines(color='0.1')\n",
    "    contour_plot1 = ax1.contourf(xlong,xlat,qvapor_diffs_vint_alltime[t,:,:], \\\n",
    "                                 levels=diff_levels, \\\n",
    "                                 cmap='RdBu',extend='both')\n",
    "    \n",
    "    ax1.text(s=\"$\\Delta$ column-int. water vapor\", x=0, y=1.01,\\\n",
    "             ha='left', va='bottom', \\\n",
    "             transform=ax1.transAxes, fontsize=fontsize)\n",
    "    ax1.text(s='hour '+'{:02d}'.format(t), x=1, y=1.01, \\\n",
    "             ha='right', va='bottom', transform=ax1.transAxes, \\\n",
    "             fontsize=fontsize)\n",
    "    #ax1.contour(xlong,xlat,qvapor_diffs_vint_alltime[t,:,:],levels=[10,20,30,40,50],colors='1',linewidths=0.25)\n",
    "    \n",
    "    fig.tight_layout()\n",
    "\n",
    "    ax1_pos = ax1.get_position()\n",
    "    cbar1 = mp.colorbar(contour_plot1)\n",
    "    cbar1.ax.tick_params(labelsize=fontsize)\n",
    "    cbar1.set_label('mm', fontsize=fontsize)\n",
    "\n",
    "    fig.savefig('hourly_vint_cwv_change_hour'+ '{:02d}'.format(t)+'.png', dpi=600, bbox_inches='tight', transparent=True)\n",
    "    mp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-16T21:59:59.294367Z",
     "start_time": "2018-04-16T21:59:01.924Z"
    }
   },
   "outputs": [],
   "source": [
    "fontsize=12\n",
    "lon_offset = 25.\n",
    "\n",
    "fig = mp.figure(figsize=(3.25,4.5))\n",
    "\n",
    "clim_levels = numpy.arange(0,56,5)\n",
    "diff_levels = numpy.arange(-1,1.1,0.2)\n",
    "\n",
    "ax1 = fig.add_subplot(211, projection=cartopy.crs.PlateCarree())\n",
    "ax1.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())\n",
    "ax1.coastlines(color='0.1')\n",
    "contour_plot1 = ax1.contourf(xlong,xlat,cont_prw_time_mean, levels=clim_levels, cmap='YlGnBu',extend='max')\n",
    "ax1.text(s='Climatological column water vapor', x=0, y=1.02,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax1.transAxes, fontsize=fontsize)\n",
    "ax1.contour(xlong,xlat,cont_prw_time_mean,levels=[10,20,30,40,50],colors='1',linewidths=0.25)\n",
    "         \n",
    "ax2 = fig.add_subplot(212, projection=cartopy.crs.PlateCarree())\n",
    "ax2.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())\n",
    "ax2.coastlines(color='0.1')\n",
    "contour_plot2 = ax2.contourf(xlong,xlat,qvapor_vint_diffs_time_mean,levels=diff_levels, cmap='RdBu',extend='both')\n",
    "ax2.text(s='Column water vapor anomalies', x=0, y=1.02,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax2.transAxes, fontsize=fontsize)\n",
    "\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1_pos = ax1.get_position()\n",
    "cbar1_ax = fig.add_axes([ax1_pos.x1+0.01,ax1_pos.y0,0.05,ax1_pos.height])\n",
    "cbar1 = mp.colorbar(contour_plot1, cax=cbar1_ax)\n",
    "cbar1.ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_label('mm', fontsize=fontsize)\n",
    "\n",
    "ax2_pos = ax2.get_position()\n",
    "cbar2_ax = fig.add_axes([ax2_pos.x1+0.01,ax2_pos.y0,0.05,ax2_pos.height])\n",
    "cbar2 = mp.colorbar(contour_plot2, cax=cbar2_ax)\n",
    "cbar2.ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_label('mm', fontsize=fontsize)\n",
    "\n",
    "fig.savefig('vint_cwv_clim_and_anomalies_time_mean.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-11T21:36:47.147096Z",
     "start_time": "2018-04-11T21:36:45.448843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 234x324 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize=12\n",
    "lon_offset = 25.\n",
    "\n",
    "fig = mp.figure(figsize=(3.25,4.5))\n",
    "\n",
    "clim_levels = numpy.arange(0,56,5)\n",
    "diff_levels = numpy.arange(-2,2.1,0.25)\n",
    "\n",
    "ax1 = fig.add_subplot(211, projection=cartopy.crs.PlateCarree())\n",
    "ax1.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())\n",
    "ax1.coastlines(color='0.1')\n",
    "contour_plot1 = ax1.contourf(xlong,xlat,cont_prw_time_mean, levels=clim_levels, cmap='YlGnBu',extend='max')\n",
    "ax1.text(s='Climatological column water vapor', x=0, y=1.02,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax1.transAxes, fontsize=fontsize)\n",
    "ax1.contour(xlong,xlat,cont_prw_time_mean,levels=[10,20,30,40,50],colors='1',linewidths=0.25)\n",
    "         \n",
    "ax2 = fig.add_subplot(212, projection=cartopy.crs.PlateCarree())\n",
    "ax2.set_extent([xlong.min()+lon_offset, xlong.max(), xlat.min(), xlat.max()], crs=cartopy.crs.PlateCarree())\n",
    "ax2.coastlines(color='0.1')\n",
    "contour_plot2 = ax2.contourf(xlong,xlat,(qvapor_vint_diffs_time_mean/cont_prw_time_mean)*100.,\\\n",
    "                             levels=diff_levels, \\\n",
    "                             cmap='RdBu',extend='both')\n",
    "ax2.text(s='Column water vapor anomalies', x=0, y=1.02,\\\n",
    "         ha='left', va='bottom', \\\n",
    "         transform=ax2.transAxes, fontsize=fontsize)\n",
    "\n",
    "\n",
    "fig.tight_layout(h_pad=2)\n",
    "\n",
    "ax1_pos = ax1.get_position()\n",
    "cbar1_ax = fig.add_axes([ax1_pos.x1+0.01,ax1_pos.y0,0.05,ax1_pos.height])\n",
    "cbar1 = mp.colorbar(contour_plot1, cax=cbar1_ax)\n",
    "cbar1.ax.tick_params(labelsize=fontsize)\n",
    "cbar1.set_label('mm', fontsize=fontsize)\n",
    "\n",
    "ax2_pos = ax2.get_position()\n",
    "cbar2_ax = fig.add_axes([ax2_pos.x1+0.01,ax2_pos.y0,0.05,ax2_pos.height])\n",
    "cbar2 = mp.colorbar(contour_plot2, cax=cbar2_ax)\n",
    "cbar2.ax.tick_params(labelsize=fontsize)\n",
    "cbar2.set_label('% change', fontsize=fontsize)\n",
    "cbar2.set_ticks([-2,-1,0,1,2])\n",
    "\n",
    "fig.savefig('vint_cwv_clim_and_anomalies_PERCENT_CHANGE_time_mean.png', dpi=600, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot water vapor climatology and differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-11T21:36:50.402928Z",
     "start_time": "2018-04-11T21:36:50.372256Z"
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-46-23931549a86b>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-23931549a86b>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    levels=\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "fontsize=12\n",
    "\n",
    "fig = mp.figure(figsize=(3,4))\n",
    "\n",
    "levels=\n",
    "contour_levels_left = numpy.arange(-50,51,10)\n",
    "plot_locations = [1,2,6,7,11,12,16,17]\n",
    "ax_list_left = []\n",
    "for i in range(plot_locations.__len__()):\n",
    "    ax = fig.add_subplot(4,5,plot_locations[i], projection=cartopy.crs.PlateCarree())\n",
    "    contour_plot_left = ax.contourf(xlong,xlat,lh_diffs_landmask[i,:,:], levels=contour_levels_left, \\\n",
    "                                    cmap='RdBu', extend='both')\n",
    "    ax.text(s='h '+hour_strings[i], x=0.05, y=0.05, transform=ax.transAxes, ha='left', va='bottom', fontsize=fontsize)\n",
    "    ax.tick_params(labelsize=fontsize)\n",
    "    ax.coastlines(resolution='110m')\n",
    "    ax_list_left.append(ax)\n",
    "ax_list_left[0].text(s='(a) $\\Delta$ latent cooling (3-hr means)', x=0, y=1.02, ha='left', va='bottom', fontsize=fontsize, transform=ax_list_left[0].transAxes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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